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MODEL-METADATA.md

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Model Metadata

The drum tool has a verb called push which requires the usage of a metadata file in your code directory to configure the creation of a DataRobot model.

Options for either tasks or inference models

  • name (required): a string used as the custom model title, try to make this unique so you can search for it later.
  • type (required): a string with the value either training or inference. Inference models are meant for solely deployment, while tasks will be able to be trained on the leaderboard.
  • environmentID (required): a hash of the execution environment to use while running your custom model. You can find a list of available execution environments here. Click on the Environment Info tab of the environment and copy the ID to your file.
  • targetType (required): a string indicating the type of target. Must be one of
    • binary
    • regression
    • anomaly
    • unstructured
    • multiclass
    • transform
  • modelID (optional): Once you have created a model for the first time, it is best practice to use custom model versions when adding code while iterating on your model. To only create a new version instead of a whole new top level model, please include a hash here for the custom model you created.
  • description (optional): A searchable note to your future self about the contents of this model. This is ignored if modelID is set.
  • majorVersion (optional, default: True): Whether the model version you are creating should be a major version update or a minor version update. If the previous model version is 2.3, a major version update would create the version 3.3, and a minor version update would create the version 2.4.

Options specific to inference models

NOTE: All options specific to inference models or tasks are ignored if modelID is set- they configure the base custom model entity only. However, they are still required to keep in the metadata file.

  • targetName (required): a string with the column of your data that your model tries to predict.
  • positiveClassLabel / negativeClassLabel: Required for binary models. If your model predicts the number 0, the negativeClassLabel dictates of your prediction that corresponds to.
  • predictionThreshold: Optional for binary models. The cutoff point between 0 and 1 that represents which label will be chosen as the predicted label.

Options specific to tasks

  • trainOnProject (optional): A hash with the pid of a project you would like to train your new model or version on. If this is supplied, the code you supplied will start to run against this pid automagically.
  • userCredentialSpecifications (optional): This is a list of credentials that will be injected from DataRobot on both fit and predict. You can get your credential IDs by looking at the URL when you click on a credential in datarobot.com/account/credentials-management. They have the following template
userCredentialSpecifications:
  - key: REQUIRED - a POSIX compatable environment name (^[_a-zA-Z][_a-zA-Z0-9]*$)
    valueFrom: REQUIRED - a valid object id pointing to your credential
    reminder: OPTIONAL - any string to help you remember what this is.

Validation Schema

The documentation for the validation schema can be found here in the DataRobot Docs site.