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Use the Library to Adapt Your Training Script
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This section contains the SageMaker distributed data parallel API documentation.
If you are a new user of this library, it is recommended you use this guide alongside
`SageMaker's Distributed Data Parallel Library
<https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel.html>`_.
The library provides framework-specific APIs for TensorFlow and PyTorch.
Select the latest or one of the previous versions of the API documentation
depending on the version of the library you use.
.. important::
The distributed data parallel library supports training jobs using CUDA 11 or later.
When you define a :class:`sagemaker.tensorflow.estimator.TensorFlow` or
:class:`sagemaker.pytorch.estimator.PyTorch`
estimator with the data parallel library enabled,
SageMaker uses CUDA 11. When you extend or customize your own training image,
you must use a base image with CUDA 11 or later. See
`SageMaker Python SDK's distributed data parallel library APIs
<https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-use-python-skd-api>`_
for more information.
Version 1.4.0, 1.4.1, 1.5.0 (Latest)
====================================
.. toctree::
:maxdepth: 1
latest/smd_data_parallel_pytorch
latest/smd_data_parallel_tensorflow
Documentation Archive
=====================
To find the API documentation for the previous versions of the library,
choose one of the following:
.. toctree::
:maxdepth: 1
archives
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