sagemaker-python-sdk
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aws
feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes
Describe the feature you'd like
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
How would this feature be used? Please describe. This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project',
'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)
Describe alternatives you've considered An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
Additional context This would provide feature parity with AWS DataScience Step functions SDK. https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
