How to execute individual steps in AzureML
The AzureML step operator is a step operator flavor provided with the ZenML
azureintegration that uses AzureML to execute individual steps of ZenML pipelines.
You should use the AzureML step operator if:
- Once your resource is created, you can head over to the
Azure Machine Learning Studioand create a compute cluster to run your pipelines.
- (Optional) Create a Service Principal for authentication. This is required if you intend to run your pipelines with a remote orchestrator.
To use the AzureML step operator, we need:
- The ZenML
azureintegration installed. If you haven't done so, runzenml integration install azure
- 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> \
# 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
@stepdecorator as follows:
from zenml.steps import step
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.
For additional configuration of the AzureML step operator, you can pass
AzureMLStepOperatorSettingswhen defining or running your pipeline. Check out the API 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 API Docs.
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.