Your model failed. That's all you get.
Regular code fails loud — a stack trace, a line number, a reason. Machine learning fails silent. A loss that quietly turns to NaN, a gradient that explodes three layers deep — and nothing tells you where.
[step 4822] loss: 0.0298
[step 4823] loss: nan
[step 4824] loss: nan
[step 4825] loss: nan
training halted.
Three steps between you and a fixed model.
No config files, no rewriting your training loop. Scroll to walk through the flow →
Hook into your tensors
Select the matrices you want tracked — weights, activations, gradients — with a single line in your training loop.
Watch it heat up
Pulse renders live, log-scale heatmaps of your selected tensors, running entirely on CPU so your GPU never feels it.
Get the root cause
An AI layer reads the heatmap alongside your code (optional) and reasons backward to the specific line and matrix behind the failure.
This is what a failure looks like on the way in.
The banding you're looking at is the tell.
Horizontal streaks across an otherwise smooth gradient field are exactly the kind of anomaly Pulse is built to catch early — long before it surfaces as a NaN loss downstream.
Join the
beta.
Free while it's small. Doesn't matter how deep your ML background is — just bring a model that's given you trouble.
Request beta access →