{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deploy a custom model with Pulumi\n", "\n", "Download this notebook\n", "\n", "This notebook outlines how to use Pulumi to deploy a Scikit-learn classifier. Before proceeding, [download the necessary assets](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/deploy_custom_model.zip) to execute the tasks in this notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize the environment\n", "\n", "Start the environment and import the DataRobot library." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import datarobot as dr\n", "\n", "os.environ['PULUMI_CONFIG_PASSPHRASE'] = 'default'\n", "\n", "assert 'DATAROBOT_API_TOKEN' in os.environ, 'Please set the DATAROBOT_API_TOKEN environment variable'\n", "assert 'DATAROBOT_ENDPOINT' in os.environ, 'Please set the DATAROBOT_ENDPOINT environment variable'\n", "\n", "dr.Client()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up a project\n", "\n", "Set up functions to create and build or destroy your Pulumi stack." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pulumi import automation as auto\n", "\n", "\n", "def stack_up(project_name: str, stack_name: str, program: callable) -> auto.Stack:\n", " # create (or select if one already exists) a stack that uses our inline program\n", " stack = auto.create_or_select_stack(\n", " stack_name=stack_name, project_name=project_name, program=program\n", " )\n", "\n", " stack.refresh(on_output=print)\n", "\n", " stack.up(on_output=print)\n", " return stack\n", "\n", "\n", "def destroy_project(stack: auto.Stack):\n", " \"\"\"Destroy pulumi project\"\"\"\n", " stack_name = stack.name\n", " stack.destroy(on_output=print)\n", "\n", " stack.workspace.remove_stack(stack_name)\n", " print(f\"stack {stack_name} in project removed\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a declarative custom model deployment\n", "\n", "Use this cell to create a custom model deployment. Add your source code to DataRobot, register the model, and then initialize the deployment. The `make_custom_deployment` function below shows how to declaratively do this. You'll see some variant of this across all application templates.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pulumi_datarobot as datarobot\n", "import pulumi\n", "\n", "\n", "def make_custom_inference_deployment():\n", " \"\"\"\n", " Deploy a trained model onto DataRobot's prediction environment.\n", "\n", " Upload source code to create a custom model version.\n", " Then create a registered model and deploy it to a prediction environment.\n", " \"\"\"\n", "\n", " # ID for Python 3.9 Scikit learn drop in environment\n", " base_environment_id = \"5e8c889607389fe0f466c72d\"\n", "\n", " # ID for the default prediction server\n", " default_prediction_server_id = \"5dd7fa2274a35f003102f60d\"\n", "\n", " custom_model_name = \"App Template Minis - Readmitted Custom Model\"\n", " registered_model_name = \"App Template Minis - Readmitted Registered Model\"\n", " deployment_name = \"App Template Minis - Readmitted Deployed Model\"\n", "\n", " deployment_files = [\n", " (\"./model_package/requirements.txt\", \"requirements.txt\"),\n", " (\"./model_package/custom.py\", \"custom.py\"),\n", " (\"./model_package/model.pkl\", \"model.pkl\"),\n", " ]\n", "\n", " custom_model = datarobot.CustomModel(\n", " resource_name=custom_model_name,\n", " files=deployment_files,\n", " base_environment_id=base_environment_id,\n", " language=\"python\",\n", " target_type=\"Binary\",\n", " target_name=\"readmitted\",\n", " )\n", "\n", " registered_model = datarobot.RegisteredModel(\n", " resource_name=registered_model_name,\n", " custom_model_version_id=custom_model.version_id,\n", " )\n", "\n", " deployment = datarobot.Deployment(\n", " resource_name=deployment_name,\n", " label=deployment_name,\n", " registered_model_version_id=registered_model.version_id,\n", " prediction_environment_id=default_prediction_server_id,\n", " )\n", "\n", " pulumi.export(\"custom_model_id\", custom_model.id)\n", " pulumi.export(\"registered_model_id\", registered_model.id)\n", " pulumi.export(\"deployment_id\", deployment.id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the Pulumi stack\n", "\n", "You can now run the Pulumi stack. Doing so takes the files in the `model_package` directory from the downloaded assets, puts them into DataRobot as a custom model, registers that model, and deploys the result." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "project_name = \"AppTemplateMinis-CustomInferenceModels\"\n", "stack_name = \"MarshallsCustomReadmissionsPredictor\"\n", "\n", "stack = stack_up(project_name, stack_name, program=make_custom_inference_deployment)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interact with outputs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from datarobot_predict.deployment import predict\n", "import pandas as pd\n", "\n", "\n", "df = pd.read_csv(\n", " \"https://s3.amazonaws.com/datarobot_public_datasets/10k_diabetes.csv\"\n", ").tail(100)\n", "\n", "\n", "deployment_id = stack.outputs().get(\"deployment_id\").value\n", "deployment = dr.Deployment.get(deployment_id)\n", "\n", "predict(deployment, data_frame=df).dataframe.head(10).iloc[:, :2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clear your work\n", "\n", "You may not be interested in keeping the custom model. Use this cell to shut down the stack, deleting any assets created in DataRobot." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "destroy_project(stack)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appendix\n", "\n", "### How does scoring code work?\n", "\n", "The code below shows what you upload so that DataRobot knows how to interact with the custom model. Since you are deploying a custom inference model with minimal transformations, it only defines two hooks to interact with the model, but you could add [others too](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-components.html). Since the example model is a standard Scikit-learn binary classifier, DataRobot can figure out how to interact with it without you defining any hooks. However, most model artifacts require some custom scoring logic, so the example includes a `custom.py` file anyway." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import Code\n", "\n", "Code(filename=\"./model_package/custom.py\", language=\"python\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What did I deploy?\n", "\n", "If you're curious how you got the fitted model in the first place, `fit_custom_model.py` shows the dataset and model fitting code. This example trains a random forest binary classifier using the 10K diabetes dataset. The code below is used to train and pickle the model. It's not important for running the template." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import Code\n", "\n", "Code(filename=\"./fit_custom_model.py\", language=\"python\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }