documentation: fix smp code example, add note for CUDA 11 to sdp

Author: Talia Chopra

doc/api/training/smd_data_parallel.rst

@@ -20,6 +20,15 @@ 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.
 
+.. important::
+   SDP only supports training jobs using CUDA 11. When you define a PyTorch or TensorFlow
+   ``Estimator`` with ``dataparallel`` parameter ``enabled`` set to ``True``,
+   it uses CUDA 11. When you extend or customize your own training image
+   you must use a CUDA 11 base image. See
+   `SageMaker Python SDK's SDP APIs
+   <https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-use-python-skd-api>`__
+   for more information.
+
 .. rubric:: Customize your training script
 
 To customize your own training script, you will need the following:

doc/api/training/smd_model_parallel.rst

@@ -13,7 +13,7 @@ Use the following sections to learn more about the model parallelism and the SMP
 
 .. important::
    SMP only supports training jobs using CUDA 11. When you define a PyTorch or TensorFlow
-   ``Estimator`` with ``smdistributed`` ``enabled``,
+   ``Estimator`` with ``modelparallel`` parameter ``enabled`` set to ``True``,
    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

doc/api/training/smd_model_parallel_general.rst

@@ -47,7 +47,7 @@ The following is an example of how you can launch a new PyTorch training job wit
             py_version='py3',
             instance_count=1,
             distribution={
-               "smdistributed": smp_options,
+               "smdistributed": {"modelparallel": smp_options},
                "mpi": mpi_options
             },
             base_job_name="SMD-MP-demo",