Importing a model from file
You can import a model from a file into WMLz for deployment and management. The model in the source file can be a previously exported WMLz model or a Spark, Scikit-learn, XGBoost, PMML, ARIMA, Seasonal ARIMA, or ONNX model trained on your distributed system.
Before you begin
For a Spark, Scikit-learn, or XGBoost model pretrained on your local distributed platform, you must use the supplied Python utility library to prepare the model for import:
- Verify that the Python environment on your local system supports XGBoost 0.90 and Scikit-learn 0.23.x releases.
- Locate the
wmlz_model_utils-2.2.202104190842-py3-none-any.whl
package in the $IML_INSTALL_DIR/imlpython/iml-pkgs directory. As its name indicates, the package file contains the WML for z/OS Python utility library. - Download the package file onto your local system where you run your own Python environment.
- Install the package into the Python environment by using the pip command:
pip install
wmlz_model_utils-2.2.202104190842-py3-none-any.whl
- If necessary, create a new Spark, Scikit-learn, or XGBoost model in your Python environment.
- Save the model to your local file system by using the WMLz Python model utility. See WML for z/OS model utility API for samples and instructions.