Distributed training with Ray Train DLCs and EFA on EKS
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The Ray Train Deep Learning Container ships the full CUDA-through-EFA stack, so you build no image. This post walks through a reproducible sample that fine-tunes Qwen2.5-1.5B with PyTorch FSDP across eight GPUs on two EFA-enabled g6.12xlarge instances on Amazon EKS, using the container exactly as published.
What usually stops PyTorch training from scaling out to a cluster is the container, not the model code. Every container needs the same CUDA runtime , a PyTorch build compiled against it, NCCL (NVIDIA Collective Communications Library), the Elastic Fabric Adapter (EFA) userspace libraries, the AWS OFI NCCL plugin that connects NCCL to EFA, and a Ray version the cluster operator agrees with. Building that image and rebuilding it each time one of those pieces moves is a project of its own, and it…
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