I got tired of switching to a browser just to see if my model was alive.

Training lives in the terminal, but monitoring lives in TensorBoard / W&B. That context switch breaks flow, especially over SSH.

So I built nn-htop — runs right in your terminal where training happens.

What it does now:

- live layer stats (mean, std, grad norm) per layer

- catches dead ReLUs / exploding grads early

- loss + lr sparkline in terminal

- zero browser, zero extra ports, works over SSH

Why TUI and not web?

Honestly I wanted to see the shape the network is drawing. Loss curves hide layer misbehavior. I've been obsessed with visualizing up to 10 dimensions — we can only really see in 3D, but if we could see more, we'd know what's happening under the hood. nn-htop is step 1.

Early and hacky, but I use it daily instead of opening a tab.

Stack: Python + Rich / Textual

Repo: https://github.com/marviecephas/nn-htop

Would love feedback from people who train on remote boxes — what stats would you want in an htop for nets? What makes you keep opening TensorBoard?

For an MLP probability of default model, what all validation techniques can be used .

Other than AUC,KS,GINI,etc standard techniques.

Also advice on how do one check for fairness/bias for such a model.

This is a credit risk model