Bring your R workloads
APPLIES TO:
Azure CLI ml extension v2 (current)
Python SDK azure-ai-ml v2 (current)
There's no Azure Machine Learning SDK for R. Instead, you'll use either the CLI or a Python control script to run your R scripts.
This article outlines the key scenarios for R that are supported in Azure Machine Learning and known limitations.
Typical R workflow
A typical workflow for using R with Azure Machine Learning:
Develop R scripts interactively using Jupyter Notebooks on a compute instance. (While you can also add Posit or RStudio to a compute instance, you can't currently access data assets in the workspace from these applications on the compute instance. So for now, interactive work is best done in a Jupyter notebook.)
- Read tabular data from a registered data asset or datastore
- Install additional R libraries
- Save artifacts to the workspace file storage
Adapt your script to run as a production job in Azure Machine Learning
- Remove any code that may require user interaction
- Add command line input parameters to the script as necessary
- Include and source the
azureml_utils.R
script in the same working directory of the R script to be executed - Use
crate
to package the model - Include the R/MLflow functions in the script to log artifacts, models, parameters, and/or tags to the job on MLflow
Submit remote asynchronous R jobs (you submit jobs via the CLI or Python SDK, not R)
- Build an environment
- Log job artifacts, parameters, tags and models
Register your model using Azure Machine Learning studio
Deploy registered R models to managed online endpoints
- Use the deployed endpoints for real-time inferencing/scoring
Known limitations
Limitation | Do this instead |
---|---|
There's no R control-plane SDK. | Use the Azure CLI or Python control script to submit jobs. |
RStudio running as a custom application (such as Posit or RStudio) within a container on the compute instance can't access workspace assets or MLflow. | Use Jupyter Notebooks with the R kernel on the compute instance. |
Interactive querying of workspace MLflow registry from R isn't supported. | |
Nested MLflow runs in R are not supported. | |
Parallel job step isn't supported. | Run a script in parallel n times using different input parameters. But you'll have to meta-program to generate n YAML or CLI calls to do it. |
Programmatic model registering/recording from a running job with R isn't supported. | |
Zero code deployment (that is, automatic deployment) of an R MLflow model is currently not supported. | Create a custom container with plumber for deployment. |
Scoring an R model with batch endpoints isn't supported. | |
Azure Machine Learning online deployment yml can only use image URIs directly from the registry for the environment specification; not pre-built environments from the same Dockerfile. | Follow the steps in How to deploy a registered R model to an online (real time) endpoint for the correct way to deploy. |
Next steps
Learn more about R in Azure Machine Learning:
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