documentation: add TF 2.4.1 support to sm distributed data parallel docs and other updates (#2179)

Author: TEChopra1000

doc/api/training/sdp_versions/v1.0.0/smd_data_parallel_pytorch.rst

@@ -155,7 +155,7 @@ PyTorch API
 
 **Supported versions:**
 
--  PyTorch 1.6
+-  PyTorch 1.6.0
 
 
 .. function:: smdistributed.dataparallel.torch.distributed.is_available()

doc/api/training/sdp_versions/v1.0.0/smd_data_parallel_tensorflow.rst

@@ -414,7 +414,7 @@ TensorFlow API
 
 .. function:: smdistributed.dataparallel.tensorflow.DistributedOptimizer
 
-   Applicable if you use the ``tf.estimator`` API in TensorFlow 2.x (2.3).
+   Applicable if you use the ``tf.estimator`` API in TensorFlow 2.x (2.3.1).
    ​
    Construct a new ``DistributedOptimizer`` , which uses TensorFlow
    optimizer under the hood for computing single-process gradient values
@@ -489,7 +489,7 @@ TensorFlow API
 
 .. function:: smdistributed.dataparallel.tensorflow.BroadcastGlobalVariablesHook
 
-   Applicable if you use the ``tf.estimator`` API in TensorFlow 2.x (2.3).
+   Applicable if you use the ``tf.estimator`` API in TensorFlow 2.x (2.3.1).
 
 
    ``SessionRunHook`` that will broadcast all global variables from root

doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.md

@@ -8,14 +8,18 @@
 
 ### PyTorch
 
-#### Add support for PyTorch 1.7
+#### Add support for PyTorch 1.7.1
 
-- Adds support for `gradient_as_bucket_view` (PyTorch 1.7 only), `find_unused_parameters` (PyTorch 1.7 only) and `broadcast_buffers` options to `smp.DistributedModel`. These options behave the same as the corresponding options (with the same names) in
+- Adds support for `gradient_as_bucket_view` (PyTorch 1.7.1 only), `find_unused_parameters` (PyTorch 1.7.1 only) and `broadcast_buffers` options to `smp.DistributedModel`. These options behave the same as the corresponding options (with the same names) in
 `torch.DistributedDataParallel` API. Please refer to the [SageMaker distributed model parallel API documentation](https://sagemaker.readthedocs.io/en/stable/api/training/smd_model_parallel_pytorch.html#smp.DistributedModel) for more information.
 
-- Adds support for `join` (PyTorch 1.7 only) context manager, which is to be used in conjunction with an instance of `smp.DistributedModel` to be able to train with uneven inputs across participating processes.
+- Adds support for `join` (PyTorch 1.7.1 only) context manager, which is to be used in conjunction with an instance of `smp.DistributedModel` to be able to train with uneven inputs across participating processes.
 
-- Adds support for `_register_comm_hook` (PyTorch 1.7 only) which will register the callable as a communication hook for DDP. NOTE: Like in DDP, this is an experimental API and subject to change.
+- Adds support for `_register_comm_hook` (PyTorch 1.7.1 only) which will register the callable as a communication hook for DDP. NOTE: Like in DDP, this is an experimental API and subject to change.
+
+### Tensorflow
+
+- Adds support for Tensorflow 2.4.1
 
 ## Bug Fixes
 
@@ -32,7 +36,7 @@ regular dicts.
 
 ### PyTorch
 
-- A performance regression was observed when training on SMP with PyTorch 1.7.1 compared to 1.6. The rootcause was found to be the slowdown in performance of `.grad` method calls in PyTorch 1.7.1 compared to 1.6. Please see the related discussion: https://github.com/pytorch/pytorch/issues/50636.
+- A performance regression was observed when training on SMP with PyTorch 1.7.1 compared to 1.6.0. The rootcause was found to be the slowdown in performance of `.grad` method calls in PyTorch 1.7.1 compared to 1.6.0. Please see the related discussion: https://github.com/pytorch/pytorch/issues/50636.
 
 
 # Sagemaker Distributed Model Parallel 1.1.0 Release Notes

doc/api/training/smp_versions/v1.1.0/smd_model_parallel_tensorflow.rst

@@ -1,7 +1,7 @@
 TensorFlow API
 ==============
 
-**Supported version: 2.3**
+**Supported version: 2.3.1**
 
 **Important**: This API document assumes you use the following import statement in your training scripts.
 

doc/api/training/smp_versions/v1.2.0/smd_model_parallel_pytorch.rst

@@ -6,7 +6,7 @@
 PyTorch API
 ===========
 
-**Supported versions: 1.7.1, 1.6**
+**Supported versions: 1.7.1, 1.6.0**
 
 This API document assumes you use the following import statements in your training scripts.
 
@@ -159,7 +159,7 @@ This API document assumes you use the following import statements in your traini
       This parameter is forwarded to the underlying ``DistributedDataParallel`` wrapper.
       Please see: `broadcast_buffer <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel>`__.
 
-   -  ``gradient_as_bucket_view (PyTorch 1.7 only)`` (default: False): To be
+   -  ``gradient_as_bucket_view (PyTorch 1.7.1 only)`` (default: False): To be
       used with ``ddp=True``. This parameter is forwarded to the underlying
       ``DistributedDataParallel`` wrapper. Please see `gradient_as_bucket_view <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel>`__.
 
@@ -257,7 +257,7 @@ This API document assumes you use the following import statements in your traini
 
    .. function:: join( )
 
-      **Available for PyTorch 1.7 only**
+      **Available for PyTorch 1.7.1 only**
 
       A context manager to be used in conjunction with an instance of
       ``smp.DistributedModel`` to be able to train with uneven inputs across

doc/api/training/smp_versions/v1.2.0/smd_model_parallel_tensorflow.rst

@@ -1,7 +1,7 @@
 TensorFlow API
 ==============
 
-**Supported version: 2.3**
+**Supported version: 2.4.1, 2.3.1**
 
 **Important**: This API document assumes you use the following import statement in your training scripts.