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documentation: doc fix #3435

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documentation: make changes in the pytorch trn1 distributed training doc
mchoi8739 Oct 21, 2022
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mchoi8739 Oct 24, 2022
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Merge branch 'master' of https://github.com/aws/sagemaker-python-sdk …
mchoi8739 Oct 24, 2022
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101 changes: 0 additions & 101 deletions doc/frameworks/pytorch/using_pytorch.rst
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Expand Up @@ -293,107 +293,6 @@ using two ``ml.p4d.24xlarge`` instances:

pt_estimator.fit("s3://bucket/path/to/training/data")

.. _distributed-pytorch-training-on-trainium:

Distributed PyTorch Training on Trainium
========================================

SageMaker Training on Trainium instances now supports the ``xla``
package through ``torchrun``. With this, you do not need to manually pass RANK,
WORLD_SIZE, MASTER_ADDR, and MASTER_PORT. You can launch the training job using the
:class:`sagemaker.pytorch.estimator.PyTorch` estimator class
with the ``torch_distributed`` option as the distribution strategy.

.. note::

This ``torch_distributed`` support is available
in the SageMaker Trainium (trn1) PyTorch Deep Learning Containers starting v1.11.0.
To find a complete list of supported versions of PyTorch Neuron, see `Neuron Containers <https://github.com/aws/deep-learning-containers/blob/master/available_images.md#neuron-containers>`_ in the *AWS Deep Learning Containers GitHub repository*.

SageMaker Debugger and Profiler are currently not supported with Trainium instances.

Adapt Your Training Script to Initialize with the XLA backend
-------------------------------------------------------------

To initialize distributed training in your script, call
`torch.distributed.init_process_group
<https://pytorch.org/docs/master/distributed.html#torch.distributed.init_process_group>`_
with the ``xla`` backend as shown below.

.. code:: python

import torch.distributed as dist

dist.init_process_group('xla')

SageMaker takes care of ``'MASTER_ADDR'`` and ``'MASTER_PORT'`` for you via ``torchrun``

For detailed documentation about modifying your training script for Trainium, see `Multi-worker data-parallel MLP training using torchrun <https://awsdocs-neuron.readthedocs-hosted.com/en/latest/frameworks/torch/torch-neuronx/tutorials/training/mlp.html?highlight=torchrun#multi-worker-data-parallel-mlp-training-using-torchrun>`_ in the *AWS Neuron Documentation*.

**Currently Supported backends:**

- ``xla`` for Trainium (Trn1) instances

For up-to-date information on supported backends for Trainium instances, see `AWS Neuron Documentation <https://awsdocs-neuron.readthedocs-hosted.com/en/latest/index.html>`_.

Launching a Distributed Training Job on Trainium
------------------------------------------------

You can run multi-node distributed PyTorch training jobs on Trainium instances using the
:class:`sagemaker.pytorch.estimator.PyTorch` estimator class.
With ``instance_count=1``, the estimator submits a
single-node training job to SageMaker; with ``instance_count`` greater
than one, a multi-node training job is launched.

With the ``torch_distributed`` option, the SageMaker PyTorch estimator runs a SageMaker
training container for PyTorch Neuron, sets up the environment, and launches
the training job using the ``torchrun`` command on each worker with the given information.

**Examples**

The following examples show how to run a PyTorch training using ``torch_distributed`` in SageMaker
on one ``ml.trn1.2xlarge`` instance and two ``ml.trn1.32xlarge`` instances:

.. code:: python

from sagemaker.pytorch import PyTorch

pt_estimator = PyTorch(
entry_point="train_ptddp.py",
role="SageMakerRole",
framework_version="1.11.0",
py_version="py38",
instance_count=1,
instance_type="ml.trn1.2xlarge",
distribution={
"torch_distributed": {
"enabled": True
}
}
)

pt_estimator.fit("s3://bucket/path/to/training/data")

.. code:: python

from sagemaker.pytorch import PyTorch

pt_estimator = PyTorch(
entry_point="train_ptddp.py",
role="SageMakerRole",
framework_version="1.11.0",
py_version="py38",
instance_count=2,
instance_type="ml.trn1.32xlarge",
distribution={
"torch_distributed": {
"enabled": True
}
}
)

pt_estimator.fit("s3://bucket/path/to/training/data")

*********************
Deploy PyTorch Models
*********************
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