Applying data loading best practices for ML training with Amazon S3 clients

This article provides best practices for optimizing data loading performance in ML training workloads using Amazon S3, with practical benchmarks and recommendations.

• Sequential read patterns with sharded datasets (100MB-1GB) significantly outperform random access patterns
• S3 Connector for PyTorch consistently delivers highest throughput among tested S3 clients
• Parallelization with tuned worker pools improves throughput; random access benefits from larger pools
• Local caching with Mountpoint for S3 dramatically improves multi-epoch training performance
• GPU saturation achieved with sequential access; random access causes latency-bound bottlenecks
• Consolidating small files into larger shards removes per-sample time-to-first-byte overhead
• Prefetching and parallelization essential for minimizing I/O bottlenecks in data pipelines

Optimizing data ingestion through sharding, sequential access patterns, and high-performance S3 clients can substantially reduce GPU idle time and improve training efficiency in cloud environments.