Handling Hardware Failures During Training: A Comparative Analysis of Fault Tolerant Training Frameworks
Sponsored by Clockwork.io
At scale, hardware failures become a statistical certainty in distributed training. Mean Time Between Failure (MTBF) decreases rapidly with cluster size, dropping from 7.9 hours at 1,024 GPUs to just 1.8 hours at 16,384 GPUs (Meta FAIR Research¹). At the same time, the cost of each failure is significant: even a single network link flap or GPU fault can cause stalls, timeouts, and eventually crash an entire job, leaving expensive clusters idle.
This webinar presents a technical comparison of three runtime resiliency strategies for distributed training. The first, checkpoint/restart, periodically saves training state to persistent storage and recovers from failures by restoring the last checkpoint and recomputing lost work. The second, live GPU migration, intercepts failures and transfers training state to spare accelerators, resuming at the same step after a short pause. The third reduces the active world size by dropping the impacted replica group, allowing training to continue immediately with altered training semantics.
The session examines the design trade-offs between these approaches across performance, training semantics, implementation complexity, and operational reliability. Attendees will come away with a clearer understanding of how each mechanism works in practice and how to evaluate them against the specific constraints of their own training infrastructure.
¹ Revisiting Reliability in Large-Scale Machine Learning Research Clusters – https://arxiv.org/html/2410.21680v2
