High-Performance File Systems

ML training workloads push modern accelerators to their limits, yet the most common performance bottleneck is not compute capacity but storage throughput. When a cluster of GPUs can process batches faster than the storage layer can deliver them, those expensive accelerators stall in I/O wait states, inflating both wall-clock training time and cost. For the AWS Certified Machine Learning Engineer – Associate exam, understanding how to diagnose and resolve these bottlenecks through storage selection is a tested skill that maps directly to the data engineering and model-training stages of the ML life cycle.

This lesson systematically compares four AWS storage services relevant to training workloads: Amazon S3, Amazon EBS, Amazon EFS, and Amazon FSx for Lustre. The right choice depends on a combination of factors, including I/O access patterns (sequential vs. random), latency sensitivity, dataset scale, file count, and whether multiple training instances require simultaneous access to the same data. Key diagnostic metrics to internalize include GPU utilization, I/O wait time, per-file read latency, aggregate read throughput (GB/s), and the ratio of compute time to data-loading time per training step.

Practical tip: If GPU utilization drops below 80% during training while CPU and I/O wait metrics spike, the storage subsystem is almost certainly the bottleneck, not the model or the optimizer.

Think of it like a factory assembly line. The GPUs are fast workers on the line, and the storage system is the conveyor belt feeding them raw materials. A slow conveyor belt means workers stand idle, regardless of how skilled they are. The following sections break down each storage option and provide a decision framework for selecting the right conveyor belt.

Amazon FSx for Lustre for training

Amazon FSx for Lustre is a fully managed ...