Chỉnh sửa

Chia sẻ qua

Bring your R workloads

  • Bài viết

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: