Stepfunctions

Latest version: v2.3.0

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2.3.0

None

2.2.0

Features
* Placeholders support 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)

2.1.0

Features
* Make Choice states chainable https://github.com/aws/aws-step-functions-data-science-sdk-python/pull/132

Bug fixes
* Make service integration ARNs partition aware https://github.com/aws/aws-step-functions-data-science-sdk-python/pull/131

2.0.0

⚠ BREAKING CHANGES

Python Support
* Drops support for Python 2

SageMaker SDK
* If your project is using the Amazon SageMaker Python SDK, it must be upgraded to **version 2.x**. Read more about upgrading to [version 2.x](https://sagemaker.readthedocs.io/en/stable/v2.html#breaking-changes).

StepFunctions SDK
* `TrainingStep` - `sagemaker.session.s3_input` has been renamed to `sagemaker.inputs.TrainingInput`
* `TuningStep` - `sagemaker.session.s3_input` has been renamed to `sagemaker.inputs.TrainingInput`
* `TrainingPipeline` - `sagemaker.session.s3_input` has been renamed to `sagemaker.inputs.TrainingInput`
* `InferencePipeline` - `sagemaker.session.s3_input` has been renamed to `sagemaker.inputs.TrainingInput`


Features
* New choice state comparators that were added to the [Amazon States Language specifications](https://states-language.net/) ([#88](https://github.com/aws/aws-step-functions-data-science-sdk-python/pull/88)) ([08a0ed7](https://github.com/aws/aws-step-functions-data-science-sdk-python/commit/08a0ed7f3aac667b5c704baf8016a8531a3509ed))
* Add support for dynamic timeout and heartbeat in Task state ([113](https://github.com/aws/aws-step-functions-data-science-sdk-python/pull/113)) ([7640db1](https://github.com/aws/aws-step-functions-data-science-sdk-python/commit/7640db1e6b81be67c35327c0c0158deb6e2f689b))

2.0.0rc1

* Updgrading to SageMaker SDK v2
* Sunsetting support for Python 2

1.1.2

* Support boolean `False` for `debugger_hook_config` parameter in `TrainingStep`
* Expose TuningStep like other SageMaker steps under the `steps` module

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