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Step Operators
How to execute individual steps in specialized environments
This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL.
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 provide 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.
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 reasonable time, combined with a Kubeflow orchestrator running on a kubernetes cluster which 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 operators to execute steps on one of the big cloud providers are provided by the following ZenML integrations:
Step Operator | Flavor | Integration | Notes |
|---|---|---|---|
sagemaker | aws | Uses SageMaker to execute steps | |
vertex | gcp | Uses Vertex AI to execute steps | |
azureml | azure | Uses AzureML to execute steps | |
spark | spark | Uses Spark on Kubernetes to execute steps in a distributed manner | |
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
You don't need to directly interact with any ZenML step operator in your code. As long as the step operator that you want to use is part of your active ZenML stack, you can simply specify it in the
@step decorator of your step:from zenml.steps import step
@step(custom_step_operator=<STEP_OPERATOR_NAME>)
def my_step(...) -> ...:
...
If some of your steps require additional hardware resources, you can specify them on your steps as described here.
Last modified 1mo ago