Revert "documentation: release notes for smdistributed.dataparallel v1.1.1 (#2280)"

This reverts commit 7fec6c1.

Author: ahsan-z-khan

doc/api/training/sdp_versions/latest.rst

@@ -1,5 +1,5 @@
 
-Version 1.1.1 (Latest)
+Version 1.1.0 (Latest)
 ======================
 
 .. toctree::

doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.md

@@ -1,41 +1,23 @@
-# 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.