documentation: smddp 1.2.1 release note / convert md to rst

doc/api/training/sdp_versions/latest.rst

@@ -1,5 +1,5 @@
 
-Version 1.2.0 (Latest)
+Version 1.2.x (Latest)
 ======================
 
 .. toctree::

doc/api/training/sdp_versions/latest/smd_data_parallel_tensorflow.rst

@@ -157,7 +157,7 @@ TensorFlow API
 
 .. rubric:: Supported versions
 
-**TensorFlow 2.3.1, 2.4.1**
+**TensorFlow 2.3.1, 2.4.1, 2.5.0**
 
 .. function:: smdistributed.dataparallel.tensorflow.init()
 

doc/api/training/smd_data_parallel.rst

@@ -101,8 +101,10 @@ Select a version to see the API documentation for version.
 Release Notes
 =============
 
-New features, bug fixes, and improvements are regularly made to the SageMaker distributed data parallel library.
+New features, bug fixes, and improvements are regularly made to the SageMaker
+distributed data parallel library.
 
-To see the the latest changes made to the library, refer to the library
-`Release Notes
-<https://github.com/aws/sagemaker-python-sdk/blob/master/doc/api/training/smd_data_parallel_release_notes/>`_.
+.. toctree::
+   :maxdepth: 1
+
+   smd_data_parallel_release_notes/smd_data_parallel_change_log

doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.rst

@@ -0,0 +1,170 @@
+Sagemaker Distributed Data Parallel 1.2.1 Release Notes
+=======================================================
+
+*Date: June. 29. 2021*
+
+**New Features:**
+
+-  Added support for TensorFlow 2.5.0.
+
+**Improvements**
+
+-  Improved performance on a single node and small clusters (2-4 nodes).
+
+**Bug fixes**
+
+-  Enable ``sparse_as_dense`` by default for SageMaker distributed data
+   parallel library for TensorFlow APIs: ``DistributedGradientTape`` and
+   ``DistributedOptimizer``.
+
+**Migration to AWS Deep Learning Containers**
+
+This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers:
+
+- TensorFlow 2.5.0 DLC release: `v1.0-tf-2.5.0-tr-py37
+  <https://github.com/aws/deep-learning-containers/releases/tag/v1.0-tf-2.5.0-tr-py37>`__
+
+  .. code::
+
+    763104351884.dkr.ecr.<region>.amazonaws.com/tensorflow-training:2.5.0-gpu-py37-cu112-ubuntu18.04-v1.0
+
+----
+
+Release History
+===============
+
+Sagemaker Distributed Data Parallel 1.2.0 Release Notes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  New features
+-  Bug Fixes
+
+**New features:**
+
+-  Support of `EFA network
+   interface <https://aws.amazon.com/hpc/efa/>`__ for distributed
+   AllReduce. For best performance, it is recommended you use an
+   instance type that supports Amazon Elastic Fabric Adapter
+   (ml.p3dn.24xlarge and ml.p4d.24xlarge) when you train a model using
+   Sagemaker Distributed data parallel.
+
+**Bug Fixes:**
+
+-  Improved performance on single node and small clusters.
+
+----
+
+Sagemaker Distributed Data Parallel 1.1.2 Release Notes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  Bug Fixes
+-  Known Issues
+
+**Bug Fixes:**
+
+-  Fixed a bug that caused some TensorFlow operations to not work with
+   certain data types. Operations forwarded from C++ have been extended
+   to support every dtype supported by NCCL.
+
+**Known Issues:**
+
+-  Sagemaker Distributed data parallel has slower throughput than NCCL
+   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 1.1.1 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:**
+
+-  Adds support for PyTorch 1.8.0 with CUDA 11.1 and CUDNN 8
+
+**Bug Fixes:**
+
+-  Fixes crash issue when importing ``smdataparallel`` before PyTorch
+
+**Improvements:**
+
+-  Update ``smdataparallel`` name in python packages, descriptions, and
+   log outputs
+
+**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.
+
+Getting Started
+
+For getting started, refer to SageMaker Distributed Data Parallel Python
+SDK Guide
+(https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-use-python-skd-api).
+
+----
+
+Sagemaker Distributed Data Parallel 1.0.0 Release Notes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  First Release
+-  Getting Started
+
+First Release
+-------------
+
+SageMaker’s distributed data parallel library extends SageMaker’s
+training capabilities on deep learning models with near-linear scaling
+efficiency, achieving fast time-to-train with minimal code changes.
+SageMaker Distributed Data Parallel:
+
+-  optimizes your training job for AWS network infrastructure and EC2
+   instance topology.
+-  takes advantage of gradient update to communicate between nodes with
+   a custom AllReduce algorithm.
+
+The library currently supports TensorFlow v2 and PyTorch via `AWS Deep
+Learning
+Containers <https://aws.amazon.com/machine-learning/containers/>`__.
+
+Getting Started
+---------------
+
+For getting started, refer to `SageMaker Distributed Data Parallel
+Python SDK
+Guide <https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-use-python-skd-api>`__.