# SageMaker AI data parallelism library release notes
<a name="data-parallel-release-notes"></a>

See the following release notes to track the latest updates for the SageMaker AI distributed data parallelism (SMDDP) library.

## The SageMaker AI distributed data parallelism library v2.5.0
<a name="data-parallel-release-notes-20241017"></a>

*Date: October 17, 2024*

**New features**
+ Added support for PyTorch v2.4.1 with CUDA v12.1.

**Integration into Docker containers distributed by the SageMaker AI model parallelism (SMP) library**

This version of the SMDDP library is migrated to [The SageMaker model parallelism library v2.6.0](model-parallel-release-notes.md#model-parallel-release-notes-20241017).

```
658645717510.dkr.ecr.<us-west-2>.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121
```

For Regions where the SMP Docker images are available, see [AWS Regions](distributed-model-parallel-support-v2.md#distributed-model-parallel-availablity-zone-v2).

**Binary file of this release**

You can download or install the library using the following URL.

```
https://smdataparallel.s3.amazonaws.com/binary/pytorch/2.4.1/cu121/2024-10-09/smdistributed_dataparallel-2.5.0-cp311-cp311-linux_x86_64.whl
```

## The SageMaker AI distributed data parallelism library v2.3.0
<a name="data-parallel-release-notes-20240611"></a>

*Date: June 11, 2024*

**New features**
+ Added support for PyTorch v2.3.0 with CUDA v12.1 and Python v3.11.
+ Added support for PyTorch Lightning v2.2.5. This is integrated into the SageMaker AI framework container for PyTorch v2.3.0.
+ Added instance type validation during import to prevent loading the SMDDP library on unsupported instance types. For a list of instance types compatible with the SMDDP library, see [Supported frameworks, AWS Regions, and instances types](distributed-data-parallel-support.md).

**Integration into SageMaker AI Framework Containers**

This version of the SMDDP library is migrated to the following [SageMaker AI Framework Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-framework-containers-sm-support-only).
+ PyTorch v2.3.0

  ```
  763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:2.3.0-gpu-py311-cu121-ubuntu20.04-sagemaker
  ```

For a complete list of versions of the SMDDP library and the pre-built containers, see [Supported frameworks, AWS Regions, and instances types](distributed-data-parallel-support.md).

**Binary file of this release**

You can download or install the library using the following URL.

```
https://smdataparallel.s3.amazonaws.com/binary/pytorch/2.3.0/cu121/2024-05-23/smdistributed_dataparallel-2.3.0-cp311-cp311-linux_x86_64.whl
```

**Other changes**
+ The SMDDP library v2.2.0 is integrated into the SageMaker AI framework container for PyTorch v2.2.0.

## The SageMaker AI distributed data parallelism library v2.2.0
<a name="data-parallel-release-notes-20240304"></a>

*Date: March 4, 2024*

**New features**
+ Added support for PyTorch v2.2.0 with CUDA v12.1.

**Integration into Docker containers distributed by the SageMaker AI model parallelism (SMP) library**

This version of the SMDDP library is migrated to [The SageMaker model parallelism library v2.2.0](model-parallel-release-notes.md#model-parallel-release-notes-20240307).

```
658645717510.dkr.ecr.<region>.amazonaws.com/smdistributed-modelparallel:2.2.0-gpu-py310-cu121
```

For Regions where the SMP Docker images are available, see [AWS Regions](distributed-model-parallel-support-v2.md#distributed-model-parallel-availablity-zone-v2).

**Binary file of this release**

You can download or install the library using the following URL.

```
https://smdataparallel.s3.amazonaws.com/binary/pytorch/2.2.0/cu121/2024-03-04/smdistributed_dataparallel-2.2.0-cp310-cp310-linux_x86_64.whl
```

## The SageMaker AI distributed data parallelism library v2.1.0
<a name="data-parallel-release-notes-20240301"></a>

*Date: March 1, 2024*

**New features**
+ Added support for PyTorch v2.1.0 with CUDA v12.1.

**Bug fixes**
+ Fixed the CPU memory leak issue in [SMDDP v2.0.1](#data-parallel-release-notes-20231207).

**Integration into SageMaker AI Framework Containers**

This version of the SMDDP library passed benchmark testing and is migrated to the following [SageMaker AI Framework Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-framework-containers-sm-support-only).
+ PyTorch v2.1.0

  ```
  763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:2.1.0-gpu-py310-cu121-ubuntu20.04-sagemaker
  ```

**Integration into Docker containers distributed by the SageMaker AI model parallelism (SMP) library**

This version of the SMDDP library is migrated to [The SageMaker model parallelism library v2.1.0](model-parallel-release-notes.md#model-parallel-release-notes-20240206).

```
658645717510.dkr.ecr.<region>.amazonaws.com/smdistributed-modelparallel:2.1.2-gpu-py310-cu121
```

For Regions where the SMP Docker images are available, see [AWS Regions](distributed-model-parallel-support-v2.md#distributed-model-parallel-availablity-zone-v2).

**Binary file of this release**

You can download or install the library using the following URL.

```
https://smdataparallel.s3.amazonaws.com/binary/pytorch/2.1.0/cu121/2024-02-04/smdistributed_dataparallel-2.1.0-cp310-cp310-linux_x86_64.whl
```

## The SageMaker AI distributed data parallelism library v2.0.1
<a name="data-parallel-release-notes-20231207"></a>

*Date: December 7, 2023*

**New features**
+ Added a new SMDDP-implementation of `AllGather` collective operation optimized for AWS compute resources and network infrastructure. To learn more, see [SMDDP `AllGather` collective operation](data-parallel-intro.md#data-parallel-allgather).
+ The SMDDP `AllGather` collective operation is compatible with PyTorch FSDP and DeepSpeed. To learn more, see [Use the SMDDP library in your PyTorch training script](data-parallel-modify-sdp-pt.md).
+ Added support for PyTorch v2.0.1

**Known issues**
+ There's a CPU memory leak issue from a gradual CPU memory increase while training with SMDDP `AllReduce` in DDP mode.

**Integration into SageMaker AI Framework Containers**

This version of the SMDDP library passed benchmark testing and is migrated to the following [SageMaker AI Framework Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-framework-containers-sm-support-only).
+ PyTorch v2.0.1

  ```
  763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:2.0.1-gpu-py310-cu118-ubuntu20.04-sagemaker
  ```

**Binary file of this release**

You can download or install the library using the following URL.

```
https://smdataparallel.s3.amazonaws.com/binary/pytorch/2.0.1/cu118/2023-12-07/smdistributed_dataparallel-2.0.2-cp310-cp310-linux_x86_64.whl
```

**Other changes**
+ Starting from this release, documentation for the SMDDP library is fully available in this *Amazon SageMaker AI Developer Guide*. In favor of the complete developer guide for SMDDP v2 housed in the *Amazon SageMaker AI Developer Guide*, documentation for the [additional reference for SMDDP v1.x](https://sagemaker.readthedocs.io/en/stable/api/training/smd_data_parallel.html) in the *SageMaker AI Python SDK documentation* is no longer supported. If you still need SMP v1.x documentation, see the following snapshot of the documentation at [SageMaker Python SDK v2.212.0 documentation](https://sagemaker.readthedocs.io/en/v2.212.0/api/training/distributed.html#the-sagemaker-distributed-data-parallel-library).