AWS Deep Learning Containers for PyTorch 1.10.2 on SageMaker
Release Date: March 07, 2022
Created On: April 15, 2022
Last Updated: April 15, 2022
AWS Deep Learning Containers for PyTorch 1.10.2 on SageMaker
AWS Deep Learning Containers for Amazon SageMaker are now available with support for PyTorch 1.10.2 and CUDA 11.3 on Ubuntu 20.04. You can launch the new versions of the Deep Learning Containers on the SageMaker service. For a complete list of frameworks and versions supported by the AWS Deep Learning Containers, see the release notes below.
This release includes container images for training and inference on CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker services, and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL, and other components to provide an optimized user experience for running deep learning workloads on AWS. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices. These new DLC are designed to be used on SageMaker Training and Inference services.
A list of available containers can be found in our documentation. Get started quickly with the AWS Deep Learning Containers using the getting-started guides and beginner to advanced level tutorials in our developer guide. You can also subscribe to our discussion forum to get launch announcements and post your questions.
Release Notes
Security Advisory
- AWS recommends that customers monitor critical security updates in the AWS Security Bulletin.
Highlights of the Release
- Introduced containers for PyTorch 1.10.2 for training and for inference on SageMaker
For latest updates, please refer to the aws/deep-learning-containers GitHub repo.
Python 3.8 Support
Python 3.8 is supported in the PyTorch Training and Inference containers.
CPU Instance Type Support
The containers support CPU instance types.
GPU Instance Type support
The containers support GPU instance types and contain the following software components for GPU support:
- CUDA 11.3
- cuDNN 8.2.0.53-1+cuda11.3
- NCCL 2.10.3-1+cuda11.3
AWS Regions support
The containers are available in the following regions:
| Region | Code |
| US East (Ohio) | us-east-2 |
| US East (N. Virginia) | us-east-1 |
| US West (Oregon) | us-west-2 |
| US West (N. California) | us-west-1 |
| Asia Pacific (Hong Kong) | ap-east-1 |
| Asia Pacific (Mumbai) | ap-south-1 |
| Asia Pacific (Osaka) | ap-northeast-3 |
| Asia Pacific (Seoul) | ap-northeast-2 |
| Asia Pacific (Singapore) | ap-southeast-1 |
| Asia Pacific (Sydney) | ap-southeast-2 |
| Asia Pacific (Tokyo) | ap-northeast-1 |
| Central (Canada) | ca-central-1 |
| EU (Frankfurt) | eu-central-1 |
| EU (Ireland) | eu-west-1 |
| EU (London) | eu-west-2 |
| EU( Paris) | eu-west-3 |
| EU (Stokholm) | eu-north-1 |
| SA (Sau Paulo) | sa-east-1 |
| Middle East (Bahrain) | me-south-1 |
| AF South (Cape Town) | af-south-1 |
| EU South (Milan) | eu-south-1 |
| China (Beijing) | cn-north-1 |
| China (Ningxia) | cn-northwest-1 |
Build and Test
- Built on: c5.18xlarge
- Tested on: c4.8xlarge, c5.18xlarge, g3.16xlarge, m4.16xlarge, p2.8xlarge, p3.16xlarge, p3dn.24xlarge, p4d.24xlarge, g4dn.xlarge
- Tested with MNIST and Resnet50/ImageNet datasets on EC2, ECS AMI (Amazon Linux AMI 2.0.20190614), and EKS AMI (amazon-eks-gpu-node-1.20-v20210914)
Known Issues
- PyTorch requires the tuning of the OMP_NUM_THREADS parameter to achieve optimal performance
- torchaudio was not included in this release because there were two unresolved open source issues previously, we will include torchaudio in the next release.