rm content

Author: mchoi8739

doc/frameworks/pytorch/using_pytorch.rst

@@ -293,121 +293,6 @@ using two ``ml.p4d.24xlarge`` instances:
 
     pt_estimator.fit("s3://bucket/path/to/training/data")
 
-.. _distributed-pytorch-training-on-trainium:
-
-Distributed Training with PyTorch Neuron on Trn1 instances
-==========================================================
-
-SageMaker Training supports Amazon EC2 Trn1 instances powered by
-`AWS Trainium <https://aws.amazon.com/machine-learning/trainium/>`_ device,
-the second generation purpose-built machine learning accelerator from AWS.
-Each Trn1 instance consists of up to 16 Trainium devices, and each
-Trainium device consists of two `NeuronCores
-<https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/arch/neuron-hardware/trn1-arch.html#trainium-architecture>`_
-in the *AWS Neuron Documentation*.
-
-You can run distributed training job on Trn1 instances.
-SageMaker 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 AWS Deep Learning Containers for PyTorch Neuron 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*.
-
-.. note::
-
-  SageMaker Debugger is currently not supported with Trn1 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 Trn1 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 Trn1 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_torch_distributed.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_torch_distributed.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")
-
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