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Sagemaker

Latest version: v2.223.0

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2.114.0

Features

* Graviton support for XGB and SKLearn frameworks
* Graviton support for PyTorch and Tensorflow frameworks
* do not expand estimator role when it is pipeline parameter
* added support for batch transform with model monitoring

Bug Fixes and Other Changes

* regex in tuning integs
* remove debugger environment var set up
* adjacent slash in s3 key
* Fix Repack step auto install behavior
* Add retry for airflow ParsingError

Documentation Changes

* doc fix

2.113.0

Features

* support torch_distributed distribution for Trainium instances

Bug Fixes and Other Changes

* bump apache-airflow from 2.4.0 to 2.4.1 in /requirements/extras

Documentation Changes

* fix kwargs and descriptions of the smdmp checkpoint function
* add the doc for the MonitorBatchTransformStep

2.112.2

Bug Fixes and Other Changes

* Update Neo-TF2.x versions to TF2.9(.2)

Documentation Changes

* fix typo in PR template

2.112.1

Bug Fixes and Other Changes

* fix(local-mode): loosen docker requirement to allow 6.0.0
* CreateModelPackage API error for Scikit-learn and XGBoost frameworkss

2.112.0

Features

* added monitor batch transform step (pipeline)

Bug Fixes and Other Changes

* Add PipelineVariable annotation to framework estimators

2.111.0

Features

* Edit test file for supporting TF 2.10 training

Bug Fixes and Other Changes

* support kms key in processor pack local code
* security issue by bumping apache-airflow from 2.3.4 to 2.4.0
* instance count retrieval logic
* Add regex for short-form sagemaker-xgboost tags
* Upgrade attrs>=20.3.0,<23
* Add PipelineVariable annotation to Amazon estimators

Documentation Changes

* add context for pytorch

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