AWS Deep Learning Containers for PyTorch 2.7 Training on EC2, ECS and EKS
AWS Deep Learning Containers (DLCs)
This release includes container images for Training on GPU, optimized for performance and scale on AWS. These Docker images have been tested with the EC2, ECS and EKS service(s), and provide stable versions of NVIDIA CUDA, 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 the EC2, ECS and EKS service.
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
Release Notes
Introduced containers for PyTorch 2.7 for Training which support EC2, ECS and EKS. For details about this release, check out our GitHub release tag.
Please refer to the official PyTorch 2.7.1 release notes here
. Added Python 3.12 support
Added PyTorch domain libraries:
torchtnt 0.2.4
torchdata 0.11.0
torchaudio 2.7.1
torchvision 0.22.1
Added CUDA 12.8 support
Added Ubuntu 22.04 support
The GPU Docker Image includes the following libraries:
CUDA 12.8.0
cuDNN 9.7.1.26
NCCL 2.26.2
EFA installer 1.40.0 (with AWS OFI NCCL embedded)
Transformer Engine 2.3
Flash Attention 2.7.4.post1
GDRCopy 2.5
Added fastai 2.8.2 support
The Dockerfile for CPU can be found here
, and the Dockerfile for GPU can be found here .
For latest updates, please refer to the aws/deep-learning-containers GitHub repo.
Security Advisory
AWS recommends that customers monitor critical security updates in the AWS Security Bulletin
Python 3.12 Support
Python 3.12 is supported in the PyTorch Training containers.
CPU Instance Type Support
The containers support x86_64 instance types.
GPU Instance Type support
The containers support GPU instance types and contain the following software components for GPU support:
CUDA 12.8
cuDNN 9.7.1.26
NCCL 2.26.2
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 |
AF South (Cape Town) |
af-south-1 |
Asia Pacific (Hong Kong) |
ap-east-1 |
Asia Pacific (Hyderabad) |
ap-south-2 |
Asia Pacific (Mumbai) |
ap-south-1 |
Asia Pacific (Osaka) |
ap-northeast-3 |
Asia Pacific (Seoul) |
ap-northeast-2 |
Asia Pacific (Tokyo) |
ap-northeast-1 |
Asia Pacific (Melbourne) |
ap-southeast-4 |
Asia Pacific (Jakarta) |
ap-southeast-3 |
Asia Pacific (Sydney) |
ap-southeast-2 |
Asia Pacific (Singapore) |
ap-southeast-1 |
Asia Pacific (Malaysia) |
ap-southeast-5 |
Asia Pacific (Thailand) |
ap-southeast-7 |
Mexico (Central) |
mx-central-1 |
Canada (Central) |
ca-central-1 |
Canada (Calgary) |
ca-west-1 |
EU (Zurich) |
eu-central-2 |
EU (Frankfurt) |
eu-central-1 |
EU (Ireland) |
eu-west-1 |
EU (London) |
eu-west-2 |
EU( Paris) |
eu-west-3 |
EU (Spain) |
eu-south-2 |
EU (Milan) |
eu-south-1 |
EU (Stockholm) |
eu-north-1 |
Israel (Tel Aviv) |
il-central-1 |
Middle East (Bahrain) |
me-south-1 |
Middle East (UAE) |
me-central-1 |
SA (Sau Paulo) |
sa-east-1 |
China (Beijing) |
cn-north-1 |
China (Ningxia) |
cn-northwest-1 |
Build and Test
Built on: c5.18xlarge
Tested on: p4d.24xlarge, p4de.24xlarge, p5.48xlarge, g4dn.4xlarge, g5.24xlarge, g5.12xlarge
Tested with Resnet50, BERT along with ImageNet datasets on EC2, ECS AMI (Amazon Linux AMI 2.0.20250605), and EKS AMI (amazon-eks-gpu-node-1.32.3-20250610)
