Distributed training with Ray Train DLCs and EFA on EKS
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This article details a method for performing distributed PyTorch training on Amazon EKS using Ray Train Deep Learning Containers (DLCs) and Elastic Fabric Adapter (EFA). The authors explain that the primary bottleneck in scaling training is typically managing complex container dependencies rather than the model code itself. To solve this, AWS provides pre-built DLCs that bundle necessary components like CUDA, NCCL, and EFA libraries, eliminating the need for custom image construction. The solution utilizes KubeRay to manage the Ray cluster structure within Kubernetes, defining head and worker pods through a single manifest. A sample implementation demonstrates fine-tuning the Qwen2.5-1.5B model across eight NVIDIA L4 GPUs using Fully Sharded Data Parallelism. By configuring environment variables and resource requests externally, users can leverage EFA for high-speed inter-node communication without modifying the container image. This approach ensures reproducible, scalable training while reducing operational overhead associated with infrastructure maintenance.
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