aws / aws-step-functions-data-science-sdk-python

Step Functions Data Science SDK for building machine learning (ML) workflows and pipelines on AWS
Apache License 2.0
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[DRAFT] feature: Support placeholders for Sagemaker parameters #154

Closed ca-nguyen closed 3 years ago

ca-nguyen commented 3 years ago

Issue #, if available: #117, #139, #94

Description of changes: Currently, it is not possible to use placeholders for Sagemaker properties. This change makes it possible to use placeholders and define SageMaker properties dynamically, after the state machine has been created (path to those params are defined in src/stepfunctions/steps/constants.py.)

With this change, it will be possible to define Sagemaker properties by passing them directly to the Sagemaker step constructor as additional args. The args that can be defined will depend on the type of step that you create.

TODO

Open questions

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StepFunctions-Bot commented 3 years ago

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ca-nguyen commented 3 years ago

I agree, this will be clearer and easier to review if each step had each own PR. I'll break it into smaller ones