KubeCon + CloudNativeCon North America 2024

Scaling ML training demands powerful GPU infrastructure, and as model sizes and training scale increases, GPU failures become an expensive risk. From outright hardware faults to subtle performance degradation, undetected GPU problems can sabotage training jobs, inflating costs and slowing development. This talk dives into GPU failure challenges in the context of ML training, particularly distributed training. We will explore the spectrum of GPU issues, and why even minor performance drops can cripple large jobs. Learn how observability (leveraging tools like NVIDIA DCGM) enables proactive problem detection through GPU health checks. Understand principles of fault-tolerant distributed training to mitigate GPU failure fallout. Drawing on cloud provider and autonomous vehicle company experience, we will share best practices for efficient identification, remediation, and prevention of GPU failures. We will also explore cutting-edge ideas like CRIU and task pre-emption for GPU workloads.