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Revert "documentation: release notes for smdistributed.dataparallel v… #2281

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2 changes: 1 addition & 1 deletion doc/api/training/sdp_versions/latest.rst
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Version 1.1.1 (Latest)
Version 1.1.0 (Latest)
======================

.. toctree::
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26 changes: 4 additions & 22 deletions doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.md
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# Sagemaker Distributed Data Parallel 1.1.1 Release Notes

* New Features
* Bug Fixes
* Known Issues

*New Features:*

* Adds support for PyTorch 1.8.1

*Bug Fixes:*

* Fixes a bug that was causing gradients from one of the worker nodes to be added twice resulting in incorrect `all_reduce` results under some conditions.

*Known Issues:*

* SageMaker distributed data parallel still is not efficient when run using a single node. For the best performance, use multi-node distributed training with `smdistributed.dataparallel`. Use a single node only for experimental runs while preparing your training pipeline.

# Sagemaker Distributed Data Parallel 1.1.0 Release Notes

* New Features
* Bug Fixes
* Improvements
* Known Issues

*New Features:*
New Features:

* Adds support for PyTorch 1.8.0 with CUDA 11.1 and CUDNN 8

*Bug Fixes:*
Bug Fixes:

* Fixes crash issue when importing `smdataparallel` before PyTorch

*Improvements:*
Improvements:

* Update `smdataparallel` name in python packages, descriptions, and log outputs

*Known Issues:*
Known Issues:

* SageMaker DataParallel is not efficient when run using a single node. For the best performance, use multi-node distributed training with `smdataparallel`. Use a single node only for experimental runs while preparing your training pipeline.

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