A PyTorch inference script produces different outputs on every run.The model weights are loaded from the same file and the input tensor never changes.This only happens after moving from training to deployment.There are no errors or warnings.
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Test classes that were once simple now require extensive setup and complex assertions. Small changes in automation break multiple tests. Maintaining coverage feels increasingly expensive. I want to understand why this happens and how teams manage it.
We collect logs, but during incidents they don’t answer key questions.Important details seem to be missing or hard to correlate.I’m trying to understand how to make logs more useful!
access torage failure
BIASed model issue
