Machine Learning is 50% guesswork right now and that is an underestimate.
Almost every developer who has tried their hand at ML has spent weeks wasting time debugging a broken model: even if the theory was perfect, the implementation almost never is. You hit the run button to start training after writing the code, walk away, and come back hours later to a flatline, a massive spike, or a model that just fails outright with zero indication of what went wrong.
I got so tired of guessing that I built Pulse to fix it.
Pulse turns the tensors and gradients inside your training run into live, log-scaled heatmaps and then reasons backwards to pinpoint fixes to makes and tell you exactly what’s going wrong. And on top of all of that, it runs when your CPU is idle so it never interferes with the overall run and your GPU can focus on training.
How it works:
- Download Pulse into your developing environment
- import auto_train() from PULSE.pulse and then run that function right before your loop.
That’s it. That’s all the setup required in the codebase.
- Then you select the weights, grads, and scalars you want to track in the Pulse window (it does loss tracking too)
- Finally, just ask the AI layer which reads the heatmaps, the graphs, and your code (optional) and pinpoints the failure.
Whether you’re training traditional architectures or debugging vibe-coded models, I’d love for you to put it through its paces and let me know what you think. The beta is officially live!
Links:
Beta Access/Feedback Form: https://docs.google.com/forms/d/e/1FAIpQLSdtbdqPC2ijZNwhDNGZBUivwvCL6gcansWIij0iA7t_WVEROA/viewform
답글 남기기