Metadata-Version: 2.3
Name: reifier
Version: 0.0.3
Summary: Neural network compilation
Author: Andis Draguns
License: MIT
Requires-Dist: numpy>=1.26.0
Requires-Dist: torch>=2.8,<2.9 ; extra == 'cpu'
Requires-Dist: torchvision>=0.23,<0.24 ; extra == 'cpu'
Requires-Dist: torch>=2.8,<2.9 ; extra == 'cu128'
Requires-Dist: torchvision>=0.23,<0.24 ; extra == 'cu128'
Requires-Python: >=3.12
Provides-Extra: cpu
Provides-Extra: cu128
Description-Content-Type: text/markdown

# Reifier

Compile algorithms into neural network circuits.

Installation:
```bash
uv pip install reifier
```

See a demo Google Colab notebook [here](https://colab.research.google.com/drive/196UXK9fwExQI07u0ZDQKMr25YbZNPilA?usp=sharing).

Circuit visualization:

<img src="https://raw.githubusercontent.com/contramont/reifier/refs/heads/main/src/reifier/examples/example_circuit.png" width="400">

Interactive visualization [here](http://draguns.me/circuit.html)

Simple example calculating xor of 5 bits:
```python
from reifier.neurons.core import const
from reifier.neurons.operations import xor
from reifier.utils.format import Bits

inputs = const('01101')
output = xor(inputs)
print(f"{Bits(inputs)} -> {Bits(output)}")
```
