Azure Machine Learning SDK for R
Data scientists and AI developers use the Azure Machine Learning SDK for R to build and run machine learning workflows with the Azure Machine Learning service.
Azure Machine Learning SDK for R uses the reticulate package to bind to Azure Machine Learning's Python SDK. By binding directly to Python, the Azure Machine Learning SDK for R allows you access to core objects and methods implemented in the Python SDK from any R environment you choose.
Main capabilities of the SDK include:
- Manage cloud resources for monitoring, logging, and organizing your machine learning experiments.
- Train models using cloud resources, including GPU-accelerated model training.
- Deploy your models as webservices on Azure Container Instances (ACI) and Azure Kubernetes Service (AKS).
Please take a look at the package website https://azure.github.io/azureml-sdk-for-r for complete documentation.
Key Features and Roadmap
|Compute||Cloud resources where you can train your machine learning models.|
|Data Plane Resources||
|Experiment||A foundational cloud resource that represents a collection of trials (individual model runs).|
|Estimator||A generic estimator to train data using any supplied training script.|
|HyperDrive||HyperDrive automates the process of running hyperparameter sweeps for an
|Model||Cloud representations of machine learning models that help you transfer models between local development environments and the
|Webservice||Models can be packaged into container images that include the runtime environment and dependencies. Models must be built into an image before you deploy them as a web service.
|Dataset||An Azure Machine Learning
Install Conda if not already installed. Choose Python 3.5 or later.
To get started, use the
remotes package to install Azure ML SDK for R from GitHub.
install_azureml() to install the compiled code from the AzureML Python SDK.
Now, you're ready to get started!
For a more detailed walk-through of the installation process, advanced options, and troubleshooting, see our Installation Guide.
To begin running experiments with Azure Machine Learning, you must establish a connection to your Azure Machine Learning workspace.
If you don't already have a workspace created, you can create one by doing:
# If you haven't already set up a resource group, set `create_resource_group = TRUE` # and set `resource_group` to your desired resource group name in order to create the resource group # in the same step. new_ws <- create_workspace(name = <workspace_name>, subscription_id = <subscription_id>, resource_group = <resource_group_name>, location = location, create_resource_group = FALSE)
After the workspace is created, you can save it to a configuration file to the local machine.
If you have an existing workspace associated with your subscription, you can retrieve it from the server by doing:
existing_ws <- get_workspace(name = <workspace_name>, subscription_id = <subscription_id>, resource_group = <resource_group_name>)
Or, if you have the workspace config.json file on your local machine, you can load the workspace by doing:
loaded_ws <- load_workspace_from_config()
Once you've accessed your workspace, you can begin running and tracking your own experiments with Azure Machine Learning SDK for R.
- R SDK package documentation: https://azure.github.io/azureml-sdk-for-r/reference/index.html
- Azure Machine Learning service: https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml
We welcome contributions from the community. If you would like to contribute to the repository, please refer to the contribution guide.