documentation: adding note about CUDA 11 to SMP

doc/api/training/smd_data_parallel.rst

@@ -20,7 +20,6 @@ with multiple GPUs. As the cluster size increases, so does the significant drop
 in performance. This drop in performance is primarily caused the communications
 overhead between nodes in a cluster.
 
-
 .. rubric:: Customize your training script
 
 To customize your own training script, you will need the following:

doc/api/training/smd_model_parallel.rst

@@ -11,6 +11,15 @@ across multiple GPUs with minimal code changes. The SMP API can be accessed thro
 
 Use the following sections to learn more about the model parallelism and the SMP library.
 
+.. important::
+   SMP only supports training jobs using CUDA 11. When you define a PyTorch or TensorFlow
+   ``Estimator`` with ``smdistributed`` ``enabled``,
+   it uses CUDA 11. When you extend or customize your own training image
+   you must use a CUDA 11 base image. See
+   `Extend or Adapt A Docker Container that Contains SMP
+   <https://integ-docs-aws.amazon.com/sagemaker/latest/dg/model-parallel-use-api.html#model-parallel-customize-container>`__
+   for more information.
+
 It is recommended to use this documentation alongside `SageMaker Distributed Model Parallel
 <http://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel.html>`__ in the Amazon SageMaker
 developer guide. This developer guide documentation includes:

doc/frameworks/pytorch/using_pytorch.rst

@@ -1,6 +1,6 @@
-###########################################
-Using PyTorch with the SageMaker Python SDK
-###########################################
+#########################################
+Use PyTorch with the SageMaker Python SDK
+#########################################
 
 With PyTorch Estimators and Models, you can train and host PyTorch models on Amazon SageMaker.