Every time a PyTorch model refuses to learn, the debugging process looks the same: Stare at the loss curve Wonder if gradients are flowing Add print statements everywhere Delete them all when it works Repeat next week After 17 years in distributed systems and SRE, I know this pattern — it is monitoring by vibes. In production infrastructure, we would never accept "the service seems slow" as a diagnostic. We measure. We trace. We verify. So I built torchdiag — five diagnostic commands that answer the actual questions. Install pip install torchdiag Enter fullscreen mode Exit fullscreen mode AddyM / torchdiag PyTorch model health diagnostics — gradient checks, dead neuron detection, training verification. Built from an SRE perspective. torchdiag PyTorch model health diagnostics — built from an SRE perspective. Stop guessing why your model isn't learning. torchdiag gives you five diagnostic commands that answer the questions that matter: Are gradients flowing? Are neurons alive?…