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Merge branch 'master' of https://github.com/aws/sagemaker-python-sdk into smdmp-1.7.0-doc
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CHANGELOG.md

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# Changelog
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## v2.78.0 (2022-03-07)
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### Features
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* TensorFlow 2.4 for Neo
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* Data Serializer
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### Bug Fixes and Other Changes
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* Style update in DataSerializer
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* Remove sagemaker_job_name from hyperparameters in TrainingStep
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* reorganize test files for workflow
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* update code to get commit_id in codepipeline
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## v2.77.1 (2022-02-25)
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### Bug Fixes and Other Changes

VERSION

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2.77.2.dev0
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2.78.1.dev0

doc/api/training/distributed.rst

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They combine software and hardware technologies to improve inter-GPU and inter-node communications.
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They extend SageMaker’s training capabilities with built-in options that require only small code changes to your training scripts.
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.. _sdp_api_docs_toc:
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The SageMaker Distributed Data Parallel Library
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. toctree::
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:maxdepth: 3
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smd_data_parallel
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sdp_versions/latest
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smd_data_parallel_use_sm_pysdk
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smd_data_parallel_release_notes/smd_data_parallel_change_log
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.. _smp_api_docs_toc:
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The SageMaker Distributed Model Parallel Library
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. toctree::
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:maxdepth: 3
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smd_data_parallel
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smd_model_parallel
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.. _smddp-version-archive:
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.. toctree::
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:maxdepth: 1
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v1_2_x.rst
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v1_1_x.rst
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v1_0_0.rst
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.. _sdp_api_docs:
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Version 1.2.x (Latest)
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#############################################
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Use the Library to Adapt Your Training Script
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#############################################
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This section contains the SageMaker distributed data parallel API documentation.
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If you are a new user of this library, it is recommended you use this guide alongside
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`SageMaker's Distributed Data Parallel Library
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<https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel.html>`_.
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The library provides framework-specific APIs for TensorFlow and PyTorch.
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Select the latest or one of the previous versions of the API documentation
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depending on the version of the library you use.
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.. important::
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The distributed data parallel library supports training jobs using CUDA 11 or later.
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When you define a :class:`sagemaker.tensorflow.estimator.TensorFlow` or
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:class:`sagemaker.pytorch.estimator.PyTorch`
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estimator with the data parallel library enabled,
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SageMaker uses CUDA 11. When you extend or customize your own training image,
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you must use a base image with CUDA 11 or later. See
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`SageMaker Python SDK's distributed data parallel library APIs
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<https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-use-python-skd-api>`_
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for more information.
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Version 1.4.0 (Latest)
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======================
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.. toctree::
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:maxdepth: 1
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latest/smd_data_parallel_pytorch.rst
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latest/smd_data_parallel_tensorflow.rst
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latest/smd_data_parallel_pytorch
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latest/smd_data_parallel_tensorflow
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Documentation Archive
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=====================
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To find the API documentation for the previous versions of the library,
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choose one of the following:
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.. toctree::
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:maxdepth: 1
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archives

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