This repo provides examples for using R with Azure Machine Learning. While an Azure Machine Learning SDK for R exists, this SDK will be deprecated in the near future. This goal of this repository is to provide examples for running R in Azure Machine Learning without a dependency on an R SDK by using supported AzureML CLI and YAML interfaces where possible.
Current examples for running R using the AML CLI include:
- Creating R environments for using AmlCompute
- Onboarding existing R code to migrate to Azure ML
[Note: the examples in the main branch are based on the AzureML CLI v2, currently in preview. This offers a simpler and more reliable approach to running R in AzureML but updates may introduce breaking changes. For a supported approach, refer to the azureml-cli-v1 branch of this repo. When CLI v2 is released, the v1 branch will be deleted. ]
To begin, start in 00-setup/.
This repo structure is as follows:
| File/folder | Description |
|---|---|
.github/workflows |
Folder for GitHub workflows used for MLOps |
00-setup/ |
Quick steps to configure an Azure ML environment for running the samples |
01-job/ |
Simple AzureML job example to train a model on the penguins dataset |
02-logging/ |
Simple AzureML job example to train a model on the penguins dataset with logging to experiment using MLflow |
utils |
Environment setup script run by GitHub workflows |
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.