feat: Adds support for Placeholders in TrainingStep to set S3 location for InputDataConfig and OutputDataConfig #142
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…propagate to Estimator
wong-a reviewed Jun 11, 2021
ca-nguyen changed the title …ining step creation with placeholders
ca-nguyen changed the title wong-a reviewed Jun 16, 2021
wong-a suggested changes Jun 16, 2021
wong-a reviewed Jun 17, 2021
wong-a suggested changes Jun 17, 2021
wong-a changed the title wong-a suggested changes Jun 17, 2021
Co-authored-by: Adam Wong <55506708+wong-a@users.noreply.github.com>
…ub.com/ca-nguyen/aws-step-functions-data-science-sdk-python into training-step-with-dynamic-output-path
…emoved unused imports
wong-a reviewed Jun 18, 2021
ca-nguyen commented Jun 18, 2021
src/stepfunctions/steps/sagemaker.py Outdated
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| from enum import Enum | ||
| from stepfunctions.inputs import ExecutionInput, StepInput | ||
| import logging |
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wong-a approved these changes Jun 18, 2021
shivlaks approved these changes Jun 18, 2021
shivlaks changed the title 
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This was referenced Jun 19, 2021
shivlaks added a commit that referenced this pull request Jun 21, 2021
bump for `v2.2.0` which includes the following changes: Features * Placeholders in TrainingStep to set S3 location for InputDataConfig and OutputDataConfig (#142) * EventBridge service integration (#147) Fixes * supplying hyperparameters to training step constructor drops hyperparameters specified in estimator (#144)
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Issue #, if available: #98, #97, #80
Description of changes:
Currently, it is not possible to specify the output path to TrainingStep. It needs to be defined at the Estimator level, which does not support placeholders. This change will make it possible to insert a placeholder output path from the TrainingStep definition and propagate it dynamically to the Estimator.
This change change also makes the TrainingStep data parameter compatible with placeholders.
There are other feature requests to make other TrainingStep arguments and ProcessingStep arguments compatible with placeholders. They will be addressed in a separate PR where the implementation could maybe be done at a higher level to avoid repetition.
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