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Drop-in environments

DataRobot provides drop-in environments in the Custom Model Workshop. Drop-in environments contain the web server Scoring Code and a file required for a custom model so that you don't need to provide them in the model's folder. The following table details the drop-in environments provided by DataRobot. Each environment is prefaced with [DataRobot] in the Environments tab of the Custom Model Workshop. 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.


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

Updated March 15, 2023
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