Distributed training with Ray Train DLCs and EFA on EKS

Executive Briefing

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.

Strategic Industry Analysis

Strategic Context: This is a direct technical implementation guide for scaling high-performance machine learning workloads on Kubernetes, critical for our enterprise AI agent and infrastructure practice.

Engineering & Architecture Perspective

Infrastructure overhead is the silent killer of AI ROI. By leveraging native AWS EFA and pre-built Ray DLCs, enterprises can bypass months of cluster orchestration headaches and immediately begin fine-tuning proprietary models on private data—if they have the architectural rigor to handle the networking complexity.

Architectural Takeaways

• Distributed Training: Impact on scalable enterprise infrastructure and operational automation.
• Amazon EKS: Impact on scalable enterprise infrastructure and operational automation.
• Elastic Fabric Adapter: Impact on scalable enterprise infrastructure and operational automation.
• Ray on Kubernetes: Impact on scalable enterprise infrastructure and operational automation.
• LLM Fine-tuning: Impact on scalable enterprise infrastructure and operational automation.

Source & Attribution

This intelligence briefing is sourced from Distributed training with Ray Train DLCs and EFA on EKS and synthesized by the MTREXA Intelligence Team.