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Learn how to implement AI-powered test and debug workflows for modern AI GPU datacenters in this 21-minute conference talk from the Open Compute Project. Discover comprehensive methodologies for capturing exhaustive logs during test cycles, decoding mixed formats, and applying AI-driven pre-analysis techniques including sanitization, deduplication, and enrichment to isolate error signatures. Explore the integration of project documentation, bug databases, and fleet-wide repositories through retrieval-augmented generation to enable contextual matching, whitelisting, and deep post-analysis using domain specifications, historical bug data, and occurrence metrics. Understand how enriched signatures feed large language models for precise root cause analysis, while stop-on-fail triggers forward critical alerts and diagnostics to team channels. Examine real-world case studies covering GPU link integrity failures, transient power inrush PGOOD anomalies, and driver reset issues that highlight emerging failure modes in AI systems. Gain insights into the planned open-source release of core log_analyze code and learn practical approaches to fighting AI system failures with AI-assisted debugging tools.
