documentation: upating version number.

Author: Talia Chopra

doc/api/training/sdp_versions/v1.1.0/smd_data_parallel_pytorch.rst renamed to doc/api/training/sdp_versions/v1.0.0/smd_data_parallel_pytorch.rst

@@ -0,0 +1,9 @@
+
+Version 1.0.0 (Latest)
+======================
+
+.. toctree::
+   :maxdepth: 1
+
+   v1.0.0/smd_data_parallel_pytorch.rst
+   v1.0.0/smd_data_parallel_tensorflow.rst

doc/api/training/sdp_versions/v1.1.0/smd_data_parallel_tensorflow.rst renamed to doc/api/training/sdp_versions/v1.0.0/smd_data_parallel_tensorflow.rst

@@ -84,7 +84,7 @@ Select a version to see the API documentation for version.
 .. toctree::
    :maxdepth: 1
 
-   sdp_versions/v1_1_0.rst
+   sdp_versions/v1_0_0.rst
 
 .. important::
    The distributed data parallel library only supports training jobs using CUDA 11. When you define a PyTorch or TensorFlow
@@ -93,4 +93,14 @@ Select a version to see the API documentation for version.
    you must use a CUDA 11 base image. 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.
+   for more information.
+
+
+Release Notes
+=============
+
+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/>`_.

doc/api/training/sdp_versions/v1_0_0.rst

@@ -0,0 +1,20 @@
+# 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).