Drop-in environments¶
DataRobot provides drop-in environments in the Custom Model Workshop. Drop-in environments contain the model requirements and the start_server.sh file 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. |
Note
All Python environments contain Scikit-Learn to help with preprocessing (if necessary), but only Scikit-Learn can make predictions on sklearn models.
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. |