AWS Walkthrough: Fine-Tune Qwen2.5 Across Nodes with Ray Train DLCs, Zero Image Builds
AWS ML Blog · rss · 2026-10-29
An AWS ML Blog deep dive on multi-node distributed training using the prebuilt Ray Train DLC on EKS — no container builds required.
The pain point: scaling PyTorch to a cluster is usually blocked by the container, not model code — CUDA, NCCL, EFA userspace libs, the aws-ofi-nccl plugin and Ray all need alignment and constant rebuilds. The DLC ships all of it assembled and tested as one unit (Ray 2.58.0, PyTorch 2.13.0, CUDA 13.0.2, DeepSpeed, flash-attn, plus allreduceperf on the path), published to ECR Public and kept patched.
Key details:
- The sample fine-tunes Qwen2.5-1.5B with FSDP over 8 NVIDIA L4 GPUs on two g6.12xlarge instances, using one RayCluster manifest and a variables file — no image ever built.
- KubeRay manages head/worker pods; a single tagless-role image serves every pod.
- A networking mismatch fails silently: NCCL drops to TCP sockets and the job crawls — the DLC's pre-integrated EFA stack avoids this.
- Extra packages install at submit time via Ray runtimeenv; tags come in vX.Y, vX.Y.Z and digest-pin flavors.
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