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

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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?…