Lift-and-shift Python code with the @step decorator
The @step decorator is a feature that converts your local
machine learning (ML) code into one or more pipeline steps. You can write your ML
function as you would for any ML project. Once tested locally or as a training
job using the @remote decorator, you can convert the function to a SageMaker pipeline
step by adding a @step decorator. You can then pass the output of the
@step-decorated function call as a step to Pipelines to create and run a pipeline. You can
chain a series of functions with the @step decorator to create a multi-step
directed acyclic graph (DAG) pipeline as well.
The setup to use the @step decorator is the same as the setup to use the
@remote decorator. You can refer to the remote function
documentation for details about how to setup
the environment and use
a configuration file to
set defaults. For more information about the @step decorator, see
sagemaker.workflow.function_step.step
To view to sample notebooks that demonstrate the use of @step decorator, see
@step
decorator sample notebooks
The following sections explain how you can annotate your local ML code with
a @step decorator to create a step, create and run a pipeline using the
step, and customize the experience for your use case.
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