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documentation: fix SMP code example, added note for CUDA 11 to SDP #2037

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Dec 14, 2020
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documentation: fix SMP code example, added note for CUDA 11 to SDP #2037

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9 changes: 9 additions & 0 deletions doc/api/training/smd_data_parallel.rst
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Expand Up @@ -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>`__
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nit: It seems there are two underscores here.

for more information.

.. rubric:: Customize your training script

To customize your own training script, you will need the following:
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2 changes: 1 addition & 1 deletion doc/api/training/smd_model_parallel.rst
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Expand Up @@ -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
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2 changes: 1 addition & 1 deletion doc/api/training/smd_model_parallel_general.rst
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Expand Up @@ -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",
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