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.readthedocs.yml renamed to .readthedocs.yaml

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version: 2
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build:
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os: ubuntu-20.04
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tools:
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python: "3.9"
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python:
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version: 3.6
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install:
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- method: pip
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path: .
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- requirements: doc/requirements.txt
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sphinx:
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configuration: doc/conf.py
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fail_on_warning: true # http://www.sphinx-doc.org/en/master/man/sphinx-build.html#id6

CHANGELOG.md

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# Changelog
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## v2.79.0 (2022-03-16)
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### Features
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* Inferentia Neuron support for HuggingFace
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* custom base job name for jumpstart models/estimators
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* Python 3.9 for readthedocs
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### Bug Fixes and Other Changes
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* container env generation for S3 URI and add test for the same
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### Documentation Changes
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* the SageMaker distributed data parallel v1.4.0 release
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* update sagemaker training compiler docstring
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* smddp doc update
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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
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* jumpstart model table
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## v2.77.0 (2022-02-22)
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### Features

VERSION

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2.77.1.dev0
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2.79.1.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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