Efficient image and model caching strategies for AI/ML and generative AI workloads on Amazon EKS

This article provides comprehensive guidance on implementing efficient image and model caching strategies for AI/ML workloads on Amazon EKS, emphasizing the critical role of storage in ML infrastructure.

• Container image caching via Bottlerocket data volumes reduces startup times up to 100%
• Secondary EBS volumes on AL2023 offer customizable, high-performance container image storage
• NVMe with RAID0 configuration provides maximum I/O performance for kubelet and containerd
• Amazon S3 delivers cost-effective, scalable storage with proven durability and availability
• S3 Express One Zone provides single-digit millisecond latency, 10x faster than S3 Standard
• FSx for Lustre scales to terabytes per second throughput with sub-millisecond latencies
• S3 Connector for PyTorch accelerates checkpoint saving by up to 40% versus EC2 storage
• Mountpoint for Amazon S3 with S3 Express One Zone accelerates ML training up to 6x
• Storage performance must align with GPU compute to avoid underutilized resources and increased costs
• FSx for Lustre with NVIDIA GPUDirect Storage removes CPU bottlenecks for faster data access

Organizations should select storage solutions based on specific workload requirements, balancing data access patterns, performance needs, and cost considerations to optimize ML training efficiency and reduce operational expenses on Amazon EKS.