👣Step Operators

Executing individual steps in specialized environments.

The step operator enables the execution of individual pipeline steps in specialized runtime environments that are optimized for certain workloads. These specialized environments can give your steps access to resources like GPUs or distributed processing frameworks like Spark.

Comparison to orchestrators: The orchestrator is a mandatory stack component that is responsible for executing all steps of a pipeline in the correct order and providing additional features such as scheduling pipeline runs. The step operator on the other hand is used to only execute individual steps of the pipeline in a separate environment in case the environment provided by the orchestrator is not feasible.

When to use it

A step operator should be used if one or more steps of a pipeline require resources that are not available in the runtime environments provided by the orchestrator. An example would be a step that trains a computer vision model and requires a GPU to run in a reasonable time, combined with a Kubeflow orchestrator running on a Kubernetes cluster that does not contain any GPU nodes. In that case, it makes sense to include a step operator like SageMaker, Vertex, or AzureML to execute the training step with a GPU.

Step Operator Flavors

Step operators to execute steps on one of the big cloud providers are provided by the following ZenML integrations:

Step Operator Flavor Integration Notes SageMaker sagemaker aws Uses SageMaker to execute steps Vertex vertex gcp Uses Vertex AI to execute steps AzureML azureml azure Uses AzureML to execute steps Spark spark spark Uses Spark on Kubernetes to execute steps in a distributed manner Custom Implementation custom Extend the step operator abstraction and provide your own implementation

If you would like to see the available flavors of step operators, you can use the command:

zenml step-operator flavor list

How to use it