Contact your DataRobot representative for information on enabling the Scoring Code feature.
The Scoring Code feature exports Scoring Code for qualifying Leaderboard models, allowing you to use DataRobot-generated models outside the platform.
The following sections describe how to work with Scoring Code:
|Scoring Code overview||Scoring Code, how you download it, and how to score with it.|
|Download Scoring Code from the Leaderboard||Downloading and configuring Scoring Code from the Leaderboard.|
|Download Scoring Code from a deployment||Downloading and configuring Scoring Code from a deployment.|
|Download time series Scoring Code||Downloading and configuring Scoring Code for a time series project.|
|Scoring at the command line||Syntax for scoring with embedded CLI.|
|Scoring Code usage examples||Examples showing how to use the Scoring Code JAR to score from the CLI and in a Java project.|
|JAR structure||The contents of the Scoring Code JAR package.|
|Generate Java models in an existing project||Retraining models that were created before the Scoring Code feature was enabled.|
|Backward-compatible Java API||Using Scoring Code with models created on different versions of DataRobot.|
|Scoring Code JAR integrations||Deploying DataRobot Scoring Code on an external platform.|
Consider the following when working with Scoring Code:
Using Scoring Code in production requires additional development efforts to implement model management and model monitoring, which the DataRobot API provides out of the box.
Exportable Java Scoring Code requires extra RAM during model building. As a result, to use this feature, you should keep your training dataset under 8GB. Projects larger than 8GB may fail due to memory issues. If you get an out-of-memory error, decrease the sample size and try again. The memory requirement does not apply during model scoring. During scoring, the only limitation on the dataset is the RAM of the machine on which the Scoring Code is run.
Scoring code is not supported in time series binary classification projects.
Keras models do not support Scoring Code by default; however, support can be enabled by having an administrator activate the Enable Scoring Code Support for Keras Models feature flag. If enabled, note that these models are not compatible with Scoring Code for Android and Snowflake.
Additional instances in which Scoring Code generation is not available include:
- Naive Bayes
- Text tokenization involving the MeCab tokenizer
- Visual AI and Location AI
Prediction Explanations support¶
Consider the following when working with Prediction Explanations for Scoring Code:
To download Prediction Explanations with Scoring Code, you must select Include Prediction Explanations during Leaderboard download or Deployment download. This option is not available for Legacy download.
Scoring Code doesn't support Prediction Explanations for time series models.