AWS Guides Distributed Training Using Ray Train DLCs and EFA on EKS
AWS published an architecture sample demonstrating distributed PyTorch training on Amazon EKS using pre-built Ray Train Deep Learning Containers (DLC) combined with Elastic Fabric Adapter (EFA) networking.
AWS detailed how to streamline multi-node distributed training by pairing pre-built Ray Train Deep Learning Containers with EFA on Amazon EKS. The solution uses KubeRay to manage worker pods, eliminating the need to compile CUDA, PyTorch, NCCL, and EFA dependencies manually. A sample walkthrough demonstrated fine-tuning Qwen2.5-1.5B with PyTorch FSDP across two g6.12xlarge instances with eight NVIDIA L4 GPUs.
Key takeaways
- AWS publishes the Ray Train DLC to package Ray 2.58.0, PyTorch 2.13.0, CUDA, and the EFA networking stack into a single maintained image for both head and worker pods.
- A sample walkthrough fine-tunes Qwen2.5-1.5B with PyTorch FSDP across eight NVIDIA L4 GPUs on two g6.12xlarge instances without rebuilding container images.
Source：AWS Machine Learning Read the original ↗
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