Distributed training with Ray Train DLCs and EFA on EKS
AWS publishes pre-built Ray Train containers that eliminate distributed training stack assembly on EKS
“What usually stops PyTorch training from scaling out to a cluster is the container, not the model code.”
AWS has released Ray Train Deep Learning Containers (DLCs) that pre-bundle the full distributed training stack—CUDA, PyTorch, NCCL, EFA libraries, and Ray—as a tested, patched unit published to ECR Public. This eliminates the significant engineering overhead of assembling and maintaining multi-node training images, shifting the work to configuration rather than image construction. The post demonstrates the approach by fine-tuning Qwen2.5-1.5B with PyTorch FSDP across eight NVIDIA L4 GPUs on two EKS nodes, with no custom image build required.