Optimizing Data Loading for ML Training on Amazon S3
Coverage of aws-ml-blog
A technical analysis by the AWS Machine Learning Blog on overcoming I/O bottlenecks in training pipelines through strategic data sharding and client selection.
In a recent technical guide, the aws-ml-blog outlines strategies for optimizing data loading performance during machine learning (ML) training, specifically when using Amazon S3 as the primary data store.
For data scientists and ML engineers, the "small file problem" is a well-known adversary. As datasets for tasks like computer vision grow to include millions of individual images, the overhead of making separate API requests for every single file can saturate network connections and leave powerful GPUs idling. This latency creates a significant bottleneck, extending training times and increasing costs. The AWS team argues that while S3 is highly scalable, achieving maximum throughput requires adapting data storage layouts to fit the object storage paradigm rather than treating it like a local file system.
The post presents a series of benchmarks comparing different client interfaces, such as Mountpoint for Amazon S3 and the Amazon S3 Connector for PyTorch. The core argument is that data preparation is just as critical as the choice of client. The authors demonstrate that consolidating raw data into larger shards-optimally between 100 MB and 1 GB-combined with sequential access patterns yields the highest throughput. Among the tools evaluated, the Amazon S3 Connector for PyTorch is highlighted as the most efficient solution for these specific training workloads.
Furthermore, the analysis emphasizes the importance of caching in multi-epoch scenarios. By keeping frequently accessed data closer to the compute resources, engineers can mitigate the latency penalties inherent in cloud object storage. This publication serves as a practical guide for engineering teams looking to reduce training time by optimizing the often-overlooked data loading layer of their MLOps pipeline.
For a detailed breakdown of the benchmarks and implementation guides, we recommend reading the full article.
Read the full post on the AWS Machine Learning Blog
Key Takeaways
- Consolidate Small Files: Grouping datasets into shards ranging from 100 MB to 1 GB significantly reduces API overhead compared to accessing millions of small files.
- Prioritize Sequential Access: Organizing data to allow for sequential reading patterns maximizes throughput and minimizes latency.
- Client Selection Matters: In the provided benchmarks, the Amazon S3 Connector for PyTorch consistently outperformed other clients for ML training data access.
- Leverage Caching: Implementing caching strategies is essential for maintaining efficiency during multi-epoch training sessions.