Release Notes

New features, bug fixes, and improvements are regularly made to the SageMaker data parallelism library.

SageMaker Distributed Data Parallel 1.8.0 Release Notes

Date: Apr. 17. 2023

Currency Updates

  • Added support for PyTorch 2.0.0.

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):

  • PyTorch 2.0.0 DLC

    763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker
    

Binary file of this version of the library for custom container users:

https://smdataparallel.s3.amazonaws.com/binary/pytorch/2.0.0/cu118/2023-03-20/smdistributed_dataparallel-1.8.0-cp310-cp310-linux_x86_64.whl

Release History

SageMaker Distributed Data Parallel 1.7.0 Release Notes

Date: Feb. 10. 2023

Currency Updates

  • Added support for PyTorch 1.13.1.

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):

  • PyTorch 1.13.1 DLC

    763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker
    

Binary file of this version of the library for custom container users:

https://smdataparallel.s3.amazonaws.com/binary/pytorch/1.13.1/cu117/2023-01-09/smdistributed_dataparallel-1.7.0-cp39-cp39-linux_x86_64.whl

SageMaker Distributed Data Parallel 1.6.0 Release Notes

Date: Dec. 15. 2022

New Features

  • New optimized SMDDP AllGather collective to complement the sharded data parallelism technique in the SageMaker model parallelism library. For more information, see Sharded data parallelism with SMDDP Collectives in the Amazon SageMaker Developer Guide.

  • Added support for Amazon EC2 ml.p4de.24xlarge instances. You can run data parallel training jobs on ml.p4de.24xlarge instances with the SageMaker data parallelism library’s AllReduce collective.

Improvements

  • General performance improvements of the SMDDP AllReduce collective communication operation.

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):

  • SageMaker training container for PyTorch v1.12.1

    763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.12.1-gpu-py38-cu113-ubuntu20.04-sagemaker
    

Binary file of this version of the library for custom container users:

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

SageMaker Distributed Data Parallel 1.5.0 Release Notes

Date: Jul. 26. 2022

Currency Updates

  • Added support for PyTorch 1.12.0.

Bug Fixes

  • Improved stability for long-running training jobs.

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):

  • PyTorch 1.12.0 DLC

    763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.12.0-gpu-py38-cu113-ubuntu20.04-sagemaker
    

Binary file of this version of the library for custom container users:

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

SageMaker Distributed Data Parallel 1.4.1 Release Notes

Date: May. 3. 2022

Currency Updates

  • Added support for PyTorch 1.11.0

Known Issues

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):

  • PyTorch 1.11.0 DLC

    763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.11.0-gpu-py38-cu113-ubuntu20.04-sagemaker
    

Binary file of this version of the library for custom container users:

https://smdataparallel.s3.amazonaws.com/binary/pytorch/1.11.0/cu113/2022-04-14/smdistributed_dataparallel-1.4.1-cp38-cp38-linux_x86_64.whl

SageMaker Distributed Data Parallel 1.4.0 Release Notes

Date: Feb. 24. 2022

New Features

  • Integrated to PyTorch DDP as a backend option

  • Added support for PyTorch 1.10.2

Breaking Changes

  • As the library is migrated into the PyTorch distributed package as a backend, the following smdistributed implementation APIs are deprecated in the SageMaker data parallal library v1.4.0 and later. Please use the PyTorch distributed APIs instead.

    • smdistributed.dataparallel.torch.distributed

    • smdistributed.dataparallel.torch.parallel.DistributedDataParallel

    • Please note the slight differences between the deprecated smdistributed.dataparallel.torch APIs and the PyTorch distributed APIs.

      • torch.distributed.barrier takes device_ids, which the smddp backend does not support.

      • The gradient_accumulation_steps option in smdistributed.dataparallel.torch.parallel.DistributedDataParallel is no longer supported. Please use the PyTorch no_sync API.

  • If you want to find documentation for the previous versions of the library (v1.3.0 or before), see the archived SageMaker distributed data parallel library documentation.

Improvements

  • Support for AllReduce Large Tensors

  • Support for the following new arguments in the PyTorch DDP class.

    • broadcast_buffers

    • find_unused_parameters

    • gradient_as_bucket_view

Bug Fixes

  • Fixed stalling issues when training on ml.p3.16xlarge.

Known Issues

  • The library currently does not support the PyTorch sub-process groups API (torch.distributed.new_group). This means that you cannot use the smddp backend concurrently with other process group backends such as NCCL and Gloo.

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):

  • PyTorch 1.10.2 DLC

    763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.10.2-gpu-py38-cu113-ubuntu20.04-sagemaker
    

SageMaker Distributed Data Parallel 1.2.2 Release Notes

Date: November. 24. 2021

New Features

  • Added support for PyTorch 1.10

  • PyTorch no_sync API support for DistributedDataParallel

  • Timeout when training stalls due to allreduce and broadcast collective calls

Bug Fixes

  • Fixed a bug that would impact correctness in the mixed dtype case

  • Fixed a bug related to the timeline writer that would cause a crash when SageMaker Profiler is enabled for single node jobs.

Improvements

  • Performance optimizations for small models on small clusters

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers:

  • PyTorch 1.10 DLC release: v1.0-pt-sagemaker-1.10.0-py38

    763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.10.0-gpu-py38-cu113-ubuntu20.04-sagemaker
    

SageMaker Distributed Data Parallel 1.2.1 Release Notes

Date: June. 29. 2021

New Features:

  • Added support for TensorFlow 2.5.0.

Improvements

  • Improved performance on a single node and small clusters (2-4 nodes).

Bug fixes

  • Enable sparse_as_dense by default for SageMaker distributed data parallel library for TensorFlow APIs: DistributedGradientTape and DistributedOptimizer.

Migration to AWS Deep Learning Containers

This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers:

  • TensorFlow 2.5.0 DLC release: v1.0-tf-2.5.0-tr-py37

    763104351884.dkr.ecr.<region>.amazonaws.com/tensorflow-training:2.5.0-gpu-py37-cu112-ubuntu18.04-v1.0
    

SageMaker Distributed Data Parallel 1.2.0 Release Notes

  • New features

  • Bug Fixes

New features:

  • Support of EFA network interface for distributed AllReduce. For best performance, it is recommended you use an instance type that supports Amazon Elastic Fabric Adapter (ml.p3dn.24xlarge and ml.p4d.24xlarge) when you train a model using SageMaker Distributed data parallel.

Bug Fixes:

  • Improved performance on single node and small clusters.


SageMaker Distributed Data Parallel 1.1.2 Release Notes

  • Bug Fixes

  • Known Issues

Bug Fixes:

  • Fixed a bug that caused some TensorFlow operations to not work with certain data types. Operations forwarded from C++ have been extended to support every dtype supported by NCCL.

Known Issues:

  • SageMaker Distributed data parallel has slower throughput than NCCL when run using a single node. For the best performance, use multi-node distributed training with smdistributed.dataparallel. Use a single node only for experimental runs while preparing your training pipeline.


SageMaker Distributed Data Parallel 1.1.1 Release Notes

  • New Features

  • Bug Fixes

  • Known Issues

New Features:

  • Adds support for PyTorch 1.8.1

Bug Fixes:

  • Fixes a bug that was causing gradients from one of the worker nodes to be added twice resulting in incorrect all_reduce results under some conditions.

Known Issues:

  • SageMaker distributed data parallel still is not efficient when run using a single node. For the best performance, use multi-node distributed training with smdistributed.dataparallel. Use a single node only for experimental runs while preparing your training pipeline.


SageMaker Distributed Data Parallel 1.1.0 Release Notes

  • New Features

  • Bug Fixes

  • Improvements

  • Known Issues

New Features:

  • Adds support for PyTorch 1.8.0 with CUDA 11.1 and CUDNN 8

Bug Fixes:

  • Fixes crash issue when importing smdataparallel before PyTorch

Improvements:

  • Update smdataparallel name in python packages, descriptions, and log outputs

Known Issues:

  • SageMaker DataParallel is not efficient when run using a single node. For the best performance, use multi-node distributed training with smdataparallel. Use a single node only for experimental runs while preparing your training pipeline.

Getting Started

For getting started, refer to SageMaker Distributed Data Parallel Python SDK Guide (https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html#data-parallel-use-python-skd-api).


SageMaker Distributed Data Parallel 1.0.0 Release Notes

  • First Release

  • Getting Started

First Release

SageMaker’s distributed data parallel library extends SageMaker’s training capabilities on deep learning models with near-linear scaling efficiency, achieving fast time-to-train with minimal code changes. SageMaker Distributed Data Parallel:

  • optimizes your training job for AWS network infrastructure and EC2 instance topology.

  • takes advantage of gradient update to communicate between nodes with a custom AllReduce algorithm.

The library currently supports TensorFlow v2 and PyTorch via AWS Deep Learning Containers.

Getting Started

For getting started, refer to SageMaker Distributed Data Parallel Python SDK Guide.