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Talia Chopra
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documentation: fixing version numbers for sm distributed mp
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doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.md

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### PyTorch
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#### Add support for PyTorch 1.7
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#### Add support for PyTorch 1.7.1
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- Adds support for `gradient_as_bucket_view` (PyTorch 1.7 only), `find_unused_parameters` (PyTorch 1.7 only) and `broadcast_buffers` options to `smp.DistributedModel`. These options behave the same as the corresponding options (with the same names) in
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`torch.DistributedDataParallel` API. Please refer to the [SageMaker distributed model parallel API documentation](https://sagemaker.readthedocs.io/en/stable/api/training/smd_model_parallel_pytorch.html#smp.DistributedModel) for more information.
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### Tensorflow
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- Adds support for Tensorflow 2.4
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- Adds support for Tensorflow 2.4.1
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## Bug Fixes
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### PyTorch
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- A performance regression was observed when training on SMP with PyTorch 1.7.1 compared to 1.6. The rootcause was found to be the slowdown in performance of `.grad` method calls in PyTorch 1.7.1 compared to 1.6. Please see the related discussion: https://github.com/pytorch/pytorch/issues/50636.
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- A performance regression was observed when training on SMP with PyTorch 1.7.1 compared to 1.6.0. The rootcause was found to be the slowdown in performance of `.grad` method calls in PyTorch 1.7.1 compared to 1.6.0. Please see the related discussion: https://github.com/pytorch/pytorch/issues/50636.
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# Sagemaker Distributed Model Parallel 1.1.0 Release Notes

doc/api/training/smp_versions/v1.2.0/smd_model_parallel_pytorch.rst

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PyTorch API
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===========
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**Supported versions: 1.7.1, 1.6**
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**Supported versions: 1.7.1, 1.6.0**
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This API document assumes you use the following import statements in your training scripts.
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doc/api/training/smp_versions/v1.2.0/smd_model_parallel_tensorflow.rst

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TensorFlow API
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==============
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**Supported version: 2.4, 2.3**
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**Supported version: 2.4.1, 2.3.1**
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**Important**: This API document assumes you use the following import statement in your training scripts.
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