This directory contains sample data and models for the Python and R programming languages. You can use these samples to perform various tasks with SAS Model Manager. To begin using the sample data and models, retrieve the files in the manner that you prefer and place all of the files in the same directory location.
This directory also contains sample workflow templates and sample code for creating custom KPI data.
Note: Contributions from users other than the SAS Model Manager support team can be added to the external-samples subdirectories. For example, external Python model samples can be placed in the /Python_Models/external-samples subdirectory.
The following sample data and models are available in the Python Models section of the samples directory:
- For a Scikit-learn decision tree, see DTree_sklearn_PyPickleModel
- For a gradient boosted decision tree with XGBoost, see Gradient_XGBoost_PyModel
The following sample data and models are available in the R Models section of the samples directory:
- For a decision tree using an R model, see DTree_Rmodel
- For a logistical regression using an R model, see LogisticReg_Rmodel
The Workflow_Integration section of the samples directory contains example workflows that can be used in conjunction with SAS Model Manager and SAS Workflow Manager.
The KPI section of the samples directory contains sample scripts for how to use DATA step and CASL code to create custom KPIs for use with SAS Model Manager.
The Container Tools section of the samples directory contains tools to help perform tasks that take place outside of SAS Viya, such as the promotion of a container across environments.
A simple Kubernetes service can be defined to allow for the use of ONNX runtime in SAS Viya 4, without requiring changes to the entire Python overlay used by other services in SAS Viya 4. This sample walks through the creation of the container, web application, service, and models that can be scored within SAS Model Manager.
This project is licensed under the Apache 2.0 License.