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Distributed training in PyTorch enables unprecedented scale, but it also introduces notoriously difficult debugging challenges. When a job with thousands of ranks hangs or slows down, identifying the root cause can feel like searching for a needle in a haystack. This lightning talk introduces the new PyTorch Distributed Debug Server, a powerful, interactive tool designed to bring clarity and control to the chaos of distributed debugging. We will provide a high-level overview of its architecture and core features, demonstrating how it provides a unified interface to inspect stack traces, analyze performance, and diagnose hangs across all workers simultaneously. Attendees will learn how this extensible server can dramatically reduce debugging time and improve the reliability of large-scale training jobs.
