Title
AWS re:Invent 2023 - Optimizing performance for machine learning training on Amazon S3 (STG358)
Summary
- Amazon S3 is the preferred storage for machine learning due to its scalability, throughput, and durability.
- S3 offers a range of storage classes and cost optimization features, including S3 Intelligent Tiering and lifecycle policies.
- New client-side optimizations and integrations, such as Mountpoint and AWS SDKs, enhance data transfer rates between S3 and ML jobs.
- A new storage class, S3 Express One Zone, provides low latency access for performance-heavy workloads.
- S3 integrates with AWS services like SageMaker, EMR, and FSx for Lustre to support various ML lifecycle stages.
- The Technology Innovation Institute (TII) is an example of a customer using S3 and SageMaker to train large-scale models.
- AWS provides managed data loading options from S3 in SageMaker, including file mode and fast file mode.
- The Mountpoint CSI driver for Kubernetes and the Amazon S3 connector for PyTorch offer specialized solutions for ML training.
- AWS Common Runtime (CRT) is used to enhance S3 performance across various clients and SDKs.
- Boto3 and the AWS CLI now have CRT integration by default on ML training instances for faster S3 transfers.
Insights
- The choice between sequential and random data access patterns is crucial for optimizing ML training performance on S3.
- S3's new storage class, S3 Express One Zone, is designed to reduce latency and improve throughput for latency-sensitive workloads.
- AWS is focusing on simplifying the integration of S3 with ML training workflows, as evidenced by the introduction of Mountpoint and the S3 connector for PyTorch.
- The AWS Common Runtime (CRT) plays a significant role in achieving high performance and reliability for S3 clients.
- SageMaker remains a highly recommended platform for ML training on AWS due to its fully managed environment and seamless integration with S3.
- The recent enhancements to Boto3 and the AWS CLI indicate AWS's commitment to improving the developer experience for ML training on S3.