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
285 stars 87 forks source link

feat: Support Placeholders with ModelStep #175

Closed ca-nguyen closed 2 years ago

ca-nguyen commented 2 years ago

Description

Add support to define Model parameters dynamically using placeholders

Fixes #117

Why is the change necessary?

Currently, it is not possible to use placeholders for Sagemaker Model parameters. The properties cannot be defined dynamically, as they need to be defined in the Model which does not accept placeholders. This change makes it possible to use placeholders for Model properties by using the parameters field that are passed down from the ModelStep.

Solution

Use the parameters field that is compatible with placeholders to define ModelStep properties. Merge the parameters that were generated from the Model with the input parameters:

Testing

Added unit and integ tests


Pull Request Checklist

Please check all boxes (including N/A items)

Testing

Documentation

Title and description


By submitting this pull request, I confirm that my contribution is made under the terms of the Apache-2.0 license.

StepFunctions-Bot commented 2 years ago

AWS CodeBuild CI Report

Powered by github-codebuild-logs, available on the AWS Serverless Application Repository