How to Accelerate Model Training and Eliminate I/O Bottlenecks for Cloud Computing

Explore strategies to accelerate model training and eliminate I/O bottlenecks in cloud computing environments. Learn about the challenges of using object storage for AI training, including low metadata performance, lack of atomic rename operations, and eventual consistency issues. Discover how to optimize storage layer I/O efficiency through data caching, prefetching, concurrent reads, and scheduling while maintaining upper-layer components. Gain insights into addressing the scalability limitations of traditional distributed file systems in containerized environments and the need for intelligent data movement with computational resources. Benefit from practical experiences shared on improving storage performance and cost-effectiveness for large-scale AI training workloads in cloud-native architectures.

How to Accelerate Model Training and Eliminate the I/O bottleneck for the Cloud - Rui Su, Juicedata