AWS Releases Ray Train Deep Learning Container for Scalable PyTorch Training on EKS
AWS has published a pre‑built Ray Train Deep Learning Container (DLC) that bundles CUDA, PyTorch, NCCL, the Elastic Fabric Adapter (EFA) userspace libraries, the AWS OFI NCCL plugin, and a compatible Ray version. The DLC is stored in Amazon ECR Public and is designed to run on Amazon Elastic Kubernetes Service (EKS) with the KubeRay operator. A single RayCluster manifest can launch a head node and multiple worker pods, each attaching to a GPU (e.g., eight NVIDIA L4 GPUs on two g6.12xlarge instances). Because the DLC already includes the EFA stack, inter‑node collective communication automatically uses EFA, eliminating the need to build and maintain a custom container image. The sample workflow demonstrates fine‑tuning the Qwen2.5‑1.5B model with PyTorch Fully Sharded Data Parallel (FSDP) across the cluster, using only the published DLC image and external configuration files.
For researchers and engineers working with large‑scale PyTorch models, the availability of a ready‑to‑use, EFA‑enabled container removes a major deployment hurdle, enabling faster experimentation and more efficient use of multi‑node GPU clusters on AWS.
Source: AWS Machine Learning Blog · aws.amazon.com