Merge branch 'master' into 1.8.1_PT_containers

Author: ahsan-z-khan

CHANGELOG.md

@@ -1,5 +1,26 @@
 # Changelog
 
+## v2.34.0 (2021-04-12)
+
+### Features
+
+ * Add support for accelerator in Clarify
+
+### Bug Fixes and Other Changes
+
+ * add Documentation for how to use
+ * enable local mode tests that were skipped
+ * add integ test for HuggingFace with TensorFlow
+
+### Documentation Changes
+
+ * release notes for smdistributed.dataparallel v1.1.1
+ * fixing the SageMaker distributed version references
+
+### Testing and Release Infrastructure
+
+ * pin version for ducutils
+
 ## v2.33.0 (2021-04-05)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.33.1.dev0
+2.34.1.dev0

doc/api/training/sdp_versions/latest.rst

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

doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.md

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