Distributed training with Ray Train DLCs and EFA on EKS

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 has nothing to do with the model you set out to train. The Ray Train Deep Learning Container (DLC) is that image, built by AWS. AWS assembles the stack, tests it as one unit, publishes it in Amazon Elastic Container Registry (Amazon ECR) Public and keeps it patched

Figure 1: One EKS cluster with a control plane managed by AWS, a CPU system node (m7i.xlarge) running the KubeRay operator and Ray head, and two EFA-enabled g6.12xlarge nodes in one Availability Zone. KubeRay builds it from one RayCluster manifest, Ray Train places eight workers, one per GPU, and inter-node collectives cross over EFA

Ray runs a job across machines as a single cluster with two roles. The Ray head coordinates and runs the global control store that tracks cluster state, the dashboard and job server you submit jobs to. Ray worker nodes supply the GPUs and run the training processes. Each worker joins the cluster by connecting to the head's address when it starts. On Kubernetes, each of these is a pod. Kubernetes has no built-in idea of a Ray cluster, so something must create the head and worker pods, start Ray in the right role in each, point the workers at the head, and replace pods that fail. KubeRay does this. It's an open source Kubernetes operator that adds a RayCluster resource: you describe a head group and a worker group in one manifest, and KubeRay builds and maintains the cluster from it