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documentation: smdistributed libraries release notes (#3543)
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‎doc/api/training/sdp_versions/latest.rst‎

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<https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-use-python-skd-api>`_
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for more information.
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Version 1.4.0, 1.4.1, 1.5.0 (Latest)
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====================================
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Version 1.4.0, 1.4.1, 1.5.0, 1.6.0 (Latest)
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===========================================
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.. toctree::
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:maxdepth: 1

‎doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.rst‎

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New features, bug fixes, and improvements are regularly made to the SageMaker
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distributed data parallel library.
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SageMaker Distributed Data Parallel 1.5.0 Release Notes
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SageMaker Distributed Data Parallel 1.6.0 Release Notes
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=======================================================
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*Date: Dec. 15. 2022*
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**New Features**
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* New optimized SMDDP AllGather collective to complement the sharded data parallelism technique
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in the SageMaker model parallelism library. For more information, see `Sharded data parallelism with SMDDP Collectives
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<https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-extended-features-pytorch-sharded-data-parallelism.html#model-parallel-extended-features-pytorch-sharded-data-parallelism-smddp-collectives>`_
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in the *Amazon SageMaker Developer Guide*.
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* Added support for Amazon EC2 ``ml.p4de.24xlarge`` instances. You can run data parallel training jobs
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on ``ml.p4de.24xlarge`` instances with the SageMaker data parallelism library’s AllReduce collective.
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**Improvements**
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* General performance improvements of the SMDDP AllReduce collective communication operation.
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**Migration to AWS Deep Learning Containers**
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This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):
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- SageMaker training container for PyTorch v1.12.1
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.. code::
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763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.12.1-gpu-py38-cu113-ubuntu20.04-sagemaker
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Binary file of this version of the library for `custom container
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<https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-bring-your-own-container>`_ users:
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.. code::
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https://smdataparallel.s3.amazonaws.com/binary/pytorch/1.12.1/cu113/2022-12-05/smdistributed_dataparallel-1.6.0-cp38-cp38-linux_x86_64.whl
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----
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Release History
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===============
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SageMaker Distributed Data Parallel 1.5.0 Release Notes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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*Date: Jul. 26. 2022*
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**Currency Updates**
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https://smdataparallel.s3.amazonaws.com/binary/pytorch/1.12.0/cu113/2022-07-01/smdistributed_dataparallel-1.5.0-cp38-cp38-linux_x86_64.whl
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----
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Release History
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===============
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SageMaker Distributed Data Parallel 1.4.1 Release Notes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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‎doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.rst‎

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distributed model parallel library.
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SageMaker Distributed Model Parallel 1.11.0 Release Notes
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SageMaker Distributed Model Parallel 1.13.0 Release Notes
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=========================================================
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*Date: Dec. 15. 2022*
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**New Features**
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* Sharded data parallelism now supports a new backend for collectives called *SMDDP Collectives*.
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For supported scenarios, SMDDP Collectives are on by default for the AllGather operation.
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For more information, see
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`Sharded data parallelism with SMDDP Collectives
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<https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-extended-features-pytorch-sharded-data-parallelism.html#model-parallel-extended-features-pytorch-sharded-data-parallelism-smddp-collectives>`_
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in the *Amazon SageMaker Developer Guide*.
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* Introduced FlashAttention for DistributedTransformer to improve memory usage and computational
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performance of models such as GPT2, GPTNeo, GPTJ, GPTNeoX, BERT, and RoBERTa.
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**Bug Fixes**
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* Fixed initialization of ``lm_head`` in DistributedTransformer to use a provided range
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for initialization, when weights are not tied with the embeddings.
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**Improvements**
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* When a module has no parameters, we have introduced an optimization to execute
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such a module on the same rank as its parent during pipeline parallelism.
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**Migration to AWS Deep Learning Containers**
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This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):
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- SageMaker training container for PyTorch v1.12.1
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.. code::
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763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.12.1-gpu-py38-cu113-ubuntu20.04-sagemaker
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Binary file of this version of the library for `custom container
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<https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-sm-sdk.html#model-parallel-bring-your-own-container>`_ users:
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- For PyTorch 1.12.0
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.. code::
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https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.12.1/build-artifacts/2022-12-08-21-34/smdistributed_modelparallel-1.13.0-cp38-cp38-linux_x86_64.whl
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----
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Release History
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===============
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SageMaker Distributed Model Parallel 1.11.0 Release Notes
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---------------------------------------------------------
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*Date: August. 17. 2022*
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**New Features**
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.. code::
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https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.12.0/build-artifacts/2022-08-12-16-58/smdistributed_modelparallel-1.11.0-cp38-cp38-linux_x86_64.whl
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----
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Release History
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===============
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https://sagemaker-distribu
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SageMaker Distributed Model Parallel 1.10.1 Release Notes
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---------------------------------------------------------

‎doc/api/training/smp_versions/latest.rst‎

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To use the library, reference the
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**Common API** documentation alongside the framework specific API documentation.
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Version 1.11.0 (Latest)
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===========================================
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Version 1.11.0, 1.13.0 (Latest)
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===============================
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To use the library, reference the Common API documentation alongside the framework specific API documentation.
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