Uploaded November 2025 | Updated September 2026, 2 weeks ago
Slides: drive.google.com/file/d/1TY9RrbO5nrrOevdzSr1_3uOVkE-vMxum/view?usp=sharing
At Ray Summit 2025, Justin Yu and Timothy Seah from Anyscale share how Ray Train eliminates the hidden bottlenecks that limit GPU utilization in deep learning workloads—unlocking high-throughput, production-grade training pipelines across heterogeneous compute.
They begin by outlining common performance killers that slow down even well-optimized training loops:
Slow or under-parallelized dataloaders
Blocking validation phases
GPU stalls caused by synchronous checkpointing
Inefficient dataset restarts during long epochs
Ray Train introduces a suite of capabilities that remove these barriers, including asynchronous checkpointing, async validation, high-performance data ingestion through Ray Data, and mid-epoch dataset resumption to keep GPUs fully saturated throughout training.
Justin and Timothy also highlight Ray Train’s rich observability ecosystem—including the Train Dashboard, built-in profiling tools, structured metrics, and unified logs—all designed to simplify debugging, tuning, and performance optimization at scale.
Attendees will learn best practices for designing fast, scalable training pipelines with Ray Train, and how to leverage heterogeneous compute environments to achieve maximum throughput in production.
Liked this video? Check out other Ray Summit breakout session recordings youtube.com/playlist?list=PLzTswPQNepXllnU0C36WtkC0dqkAoDulh
Subscribe to our YouTube channel to stay up-to-date on the future of AI! youtube.com/c/anyscale
🔗 Connect with us:
LinkedIn: linkedin.com/company/joinanyscale
X: https://x.com/anyscalecompute
Website: anyscale.com
Slides: drive.google.com/file/d/1TY9RrbO5nrrOevdzSr1_3uOVkE-vMxum/view?usp=sharing
At Ray Summit 2025, Justin Yu and Timothy Seah from Anyscale share how Ray Train eliminates the hidden bottlenecks that limit GPU utilization in deep learning workloads—unlocking high-throughput, production-grade training pipelines across heterogeneous compute.
They begin by outlining common performance killers that slow down even well-optimized training loops:
Slow or under-parallelized dataloaders
Blocking validation phases
GPU stalls caused by synchronous checkpointing
Inefficient dataset restarts during long epochs
Ray Train introduces a suite of capabilities that remove these barriers, including asynchronous checkpointing, async validation, high-performance data ingestion through Ray Data, and mid-epoch dataset resumption to keep GPUs fully saturated throughout training.
Justin and Timothy also highlight Ray Train’s rich observability ecosystem—including the Train Dashboard, built-in profiling tools, structured metrics, and unified logs—all designed to simplify debugging, tuning, and performance optimization at scale.
Attendees will learn best practices for designing fast, scalable training pipelines with Ray Train, and how to leverage heterogeneous compute environments to achieve maximum throughput in production.
Liked this video? Check out other Ray Summit breakout session recordings youtube.com/playlist?list=PLzTswPQNepXllnU0C36WtkC0dqkAoDulh
Subscribe to our YouTube channel to stay up-to-date on the future of AI! youtube.com/c/anyscale
🔗 Connect with us:
LinkedIn: linkedin.com/company/joinanyscale
X: https://x.com/anyscalecompute
Website: anyscale.com
