AWS Releases Guide for Distributed PyTorch Training on EKS Using Ray Train DLC and EFA
Current overview (not historical)
AWS has published a step‑by‑step guide that enables users to run distributed PyTorch training on Amazon EKS by leveraging Ray Train DLCs and Elastic Fabric Adapter (EFA). The workflow eliminates the need to build custom training images; instead, the AWS‑published Ray Train DLC is used directly, which includes the EFA networking stack. Key requirements for the setup are that the Kubernetes resource requests must equal the limits, placing workers in the Guaranteed QoS class, and that the “num-gpus” value is derived from the GPU request variable. These constraints ensure that Ray’s capacity view remains synchronized with Kubernetes, preventing resource mismatches during training. The guide provides a complete pipeline from cluster provisioning to job submission, offering a streamlined path for developers to scale PyTorch workloads on EKS with low‑latency inter‑node communication via EFA.
Written with AI from the reporting · updated 10/28/2026
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- 1Unclassified fact stationOct 29, 00:00recorded Oct 29, 03:11single source
使用 Ray Train DLC 和 EFA 在 EKS 上分布式训练 Qwen2.5-1.5B
Read original source ↗ AWS Machine Learning BlogReplay this moment in the explorerEvidence & revisions · 1 source records
- Subject：
- aws
- Action：
- 发布分布式训练指南
- Object：
- 在 EKS 集群上使用 Ray Train DLC 和 EFA 实现 Qwen2.5-1.5B 的分布式训练
- Occurred (fact side)：
- Oct 29, 00:00
- Published (valid side)：
- Oct 29, 02:19
- First seen (known side)：
- Oct 29, 03:11
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- AWS Machine Learning BlogSelectedFirst-party sourceRelated fact: single sourceAWS Releases Ray Train Deep Learning Container for Scalable PyTorch Training on EKS
Report first recorded Oct 29, 03:11
AWS has published a pre‑built Ray Train Deep Learning Container (DLC) that bundles CUDA, PyTorch, NCCL, the Elastic Fabric Adapter (EFA) userspace libraries, the AWS OFI NCCL plugin, and a compatible Ray version. The DLC is stored in Amazon ECR Public and is designed to run on Amazon Elastic Kubernetes Service (EKS) with the KubeRay operator. A single RayCluster manifest can launch a head node and multiple worker pods, each attaching to a GPU (e.g., eight NVIDIA L4 GPUs on two g6.12xlarge instances). Because the DLC already includes the EFA stack, inter‑node collective communication automatically uses EFA, eliminating the need to build and maintain a custom container image. The sample workflow demonstrates fine‑tuning the Qwen2.5‑1.5B model with PyTorch Fully Sharded Data Parallel (FSDP) across the cluster, using only the published DLC image and external configuration files.
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