> \

--environment_name=<AZURE_ENVIRONMENT_NAME> \# only pass these if using Service Principal Authentication

#   --tenant_id=<TENANT_ID> \

#   --service_principal_id=<SERVICE_PRINCIPAL_ID> \

#   --service_principal_password=<SERVICE_PRINCIPAL_PASSWORD> \

# Add the step operator to the active stack

zenml stack update -s <NAME>

Once you added the step operator to your active stack, you can use it to execute individual steps of your pipeline by specifying it in the @step decorator as follows:

from zenml import step

@step(step_operator= <NAME>)

def trainer(...) -> ...:

"""Train a model."""

# This step will be executed in AzureML.

ZenML will build a Docker image called <CONTAINER_REGISTRY_URI>/zenml:<PIPELINE_NAME> which includes your code and use it to run your steps in AzureML. Check out this page if you want to learn more about how ZenML builds these images and how you can customize them.

Additional configuration

For additional configuration of the AzureML step operator, you can pass AzureMLStepOperatorSettings when defining or running your pipeline. Check out the SDK docs for a full list of available attributes and this docs page for more information on how to specify settings.

For more information and a full list of configurable attributes of the AzureML step operator, check out the SDK Docs .

Enabling CUDA for GPU-backed hardware

Note that if you wish to use this step operator to run steps on a GPU, you will need to follow the instructions on this page to ensure that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.

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Last updated 18 days ago