NEWS · CODING · #2743
AWS Ray Train DLC (train-ml-cuda-v1.1) enables EFA-backed multi-node PyTorch on EKS
AWS provides a Ray Train Deep Learning Container (public.ecr.aws/deep-learning-containers/ray:train-ml-cuda-v1.1) that bundles CUDA, NCCL, EFA userspace libraries and Ray, allowing multi-node PyTorch training on EKS without building custom images. A provided sample shows fine-tuning Qwen2.5-1.5B with PyTorch FSDP across eight NVIDIA L4 GPUs on two g6.12xlarge instances using KubeRay and EFA.
KEY POINTS
- AWS provides a Ray Train Deep Learning Container (public.ecr.aws/deep-learning-containers/ray:train-ml-cuda-v1.1) that bundles CUDA, NCCL, EFA userspace libraries and Ray, allowing multi-node PyTorch training on EKS without building custom images.
- A provided sample shows fine-tuning Qwen2.5-1.5B with PyTorch FSDP across eight NVIDIA L4 GPUs on two g6.12xlarge instances using KubeRay and EFA.
- Removing per-cluster container assembly and testing reduces deployment overhead and preserves high performance for multi-node GPU training over EFA.
WHY IT MATTERS
Removing per-cluster container assembly and testing reduces deployment overhead and preserves high performance for multi-node GPU training over EFA.