AzureML

Executing individual steps in AzureML.

AzureML offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's AzureML step operator allows you to submit individual steps to be run on AzureML compute instances.

When to use it

You should use the AzureML step operator if:

one or more steps of your pipeline require computing resources (CPU, GPU, memory) that are not provided by your orchestrator.

you have access to AzureML. If you're using a different cloud provider, take a look at the SageMaker or Vertex step operators.

How to deploy it

Create a Machine learning resource on Azure .

Once your resource is created, you can head over to the Azure Machine Learning Studio and create a compute cluster to run your pipelines.

Create an environment for your pipelines. Follow this guide to set one up.

(Optional) Create a Service Principal for authentication. This is required if you intend to run your pipelines with a remote orchestrator.

How to use it

To use the AzureML step operator, we need:

The ZenML azure integration installed. If you haven't done so, runCopyzenml integration install azure

An AzureML compute cluster and environment. See the deployment section for detailed instructions.

A remote artifact store as part of your stack. This is needed so that both your orchestration environment and AzureML can read and write step artifacts. Check out the documentation page of the artifact store you want to use for more information on how to set that up and configure authentication for it.

We can then register the step operator and use it in our active stack:

zenml step-operator register <NAME> \

--flavor=azureml \

--subscription_id=<AZURE_SUBSCRIPTION_ID> \

--resource_group=<AZURE_RESOURCE_GROUP> \

--workspace_name=<AZURE_WORKSPACE_NAME> \

--compute_target_name=<AZURE_COMPUTE_TARGET_NAME> \

--environment_name=<AZURE_ENVIRONMENT_NAME> \