HOW WE KEEP GPUS RELIABLE ACROSS DATABRICKS AI

Summary from the TLDR Dev newsletter

GPU failures during distributed training can primarily be categorized into crashed jobs, silent slowdowns, and numerical corruption, each posing distinct challenges for maintaining reliable performance. To address these issues, a comprehensive health check system at Databricks AI uses both proactive stress testing of workloads and continuous monitoring to detect and mitigate failures across the GPU infrastructure.