pyfit 1.0.1

A minimalist neural networks library built on a tiny autograd engine

pyfit

A minimalist neural networks library built on a tiny autograd engine. Very much inspired by the micrograd library created by Andrej Karpathy.

Overview

This project aims to:

• demonstrate automatic differentiation, a core concept of modern Deep Learning frameworks like PyTorch and TensorFlow;
• define a simple API for training neural nets, somehow mimicking Keras and PyTorch Ignite;
• follow good coding practices, including type annotations and unit tests.

Demonstration

The demo notebook showcases what pyfit is all about.

Features

• Autograd engine [ source | tests ]
• Neural networks API [ source | tests ]
• Metrics [ source | tests ]
• Optimizers [ source | tests ]
• Data utilities [ source | tests ]
• Training API [ source | tests ]

Development Notes

Checking the code

pyfit uses the following tools:

• black for code formatting.
• pylint and mypy for linting.
• pytest for testing.

Run the following commands in project root folder to check the codebase.

> python -m pylint pyfit tests  # linting (including type checks)
> python -m mypy .              # type checks only
> python -m pytest              # test suite

Uploading the package to PyPI

> python setup.py sdist bdist_wheel
> python -m twine upload dist/* --skip-existing