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Drop-in environments

Availability information

Custom execution environment management in NextGen is on by default.

Feature flag: Enable Custom Execution Environments NextGen UI

DataRobot provides drop-in environments in the model workshop, defining the required libraries and providing a start_server.sh file. The following table details the drop-in environments provided by DataRobot and links to the template in the DRUM repository. Each environment is prefaced with [DataRobot] in the Environment section of the Model workshop's Assemble tab.

Custom model drop-in environments

You can select these drop-in environments when you create a custom model:

Environment name & example Model compatibility & artifact file extension
Python 3 ONNX Drop-In ONNX models (.onnx)
Python 3 PMML Drop-In PMML models (.pmml)
Python 3 PyTorch Drop-In PyTorch models (.pth)
Python 3 Scikit-Learn Drop-In Scikit-Learn models (.pkl)
Python 3 XGBoost Drop-In Native XGBoost models (.pkl)
Python 3 Keras Drop-In Keras models backed by tensorflow (.h5)
Java Drop-In DataRobot Scoring Code models (.jar)
R Drop-in Environment R models trained using CARET (.rds)
Due to the time required to install all libraries recommended by CARET, only model types that are also package names are installed (e.g., brnn, glmnet). Make a copy of this environment and modify the Dockerfile to install the additional, required packages. To decrease build times when you customize this environment, you can also remove unnecessary lines in the # Install caret models section, installing only what you need. Review the CARET documentation to check if your model's method matches its package name. (Log in to GitHub before clicking this link.)
Julia Drop-In* Julia models (.jlso)
* The Julia drop-in environment isn't officially supported; it is provided as an example.

Note

All Python environments contain Scikit-Learn to help with preprocessing (if necessary), but only Scikit-Learn can make predictions on sklearn models.

Custom model environment variables

When you use a drop-in environment, your custom model code can reference several environment variables injected to facilitate access to the DataRobot Client and MLOps Connected Client:

Environment Variable Description
MLOPS_DEPLOYMENT_ID If a custom model is running in deployment mode (i.e., the custom model is deployed), the deployment ID is available.
DATAROBOT_ENDPOINT If a custom model has public network access, the DataRobot endpoint URL is available.
DATAROBOT_API_TOKEN If a custom model has public network access, your DataRobot API token is available.

Updated June 18, 2024