Metadata-Version: 2.4
Name: torchui
Version: 0.0.1
Summary: See inside your PyTorch model: an in-browser visualizer for weights, gradients, the computation graph, and what changes during training.
Keywords: pytorch,torch,visualization,neural-network,interpretability,education,autograd
Author: Don Pinkus
Author-email: Don Pinkus <don.pinkus@gmail.com>
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.10
Project-URL: Homepage, https://github.com/donpinkus/torchui
Project-URL: Issues, https://github.com/donpinkus/torchui/issues
Project-URL: Repository, https://github.com/donpinkus/torchui
Description-Content-Type: text/markdown

# torchui

**See inside your PyTorch model, in the browser, with one line of code.**

```python
import torchui
torchui.watch(model)
```

torchui is a learning-oriented visualizer for PyTorch. It is not a production
experiment tracker. It exists to make the fundamentals *visible*: what a
network actually is as a set of matrices, how data flows through it, what the
autograd computation graph looks like, and what really changes in the weights
during training.

## What it will show

- **Architecture** as a layer diagram with real tensor shapes flowing through it.
- **Weights as matrices**: heatmaps of every parameter, conv kernels as images,
  and a scrubber to watch weights drift from their initial values step by step.
- **The computation graph**, taken from the real autograd graph (`grad_fn`),
  so what you see is what backprop traverses.
- **Training dynamics**: loss, per-layer gradient norms, update ratios,
  activation distributions, and dead-ReLU fractions over time.
- **Single-sample traces**: pick one input and see every intermediate
  activation on its way to the output.

## How it works

`torchui.watch(model)` registers forward hooks and gradient hooks on the model,
observes `backward()` and `optimizer.step()` to find the loss and step
boundaries, and records everything to a run directory on disk. A small
background server serves a static single-page viewer that reads that
directory, so you can watch live or replay any past run with
`torchui runs/<name>`.

## Status

Early development. This release reserves the package name; the first working
version is on its way.

## License

MIT
