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maxvanspengler / hyperbolic_learning_library

An extension of the PyTorch library containing various tools for performing deep learning in hyperbolic space.
MIT License
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Hyperbolic Learning Library

Documentation Status Unit Tests Code style: black isort: checked

An extension of the PyTorch library containing various tools for performing deep learning in hyperbolic space.

Contents:

Documentation

Visit our documentation for tutorials and more.

Installation

The Hyperbolic Learning Library was written for Python 3.10+ and PyTorch 1.11+.

It's recommended to have a working PyTorch installation before setting up HypLL:

Start by setting up a Python virtual environment:

python -venv .env

Activate the virtual environment on Linux and MacOs:

source .env/bin/activate

Or on Windows:

.env/Scripts/activate

Finally, install HypLL from PyPI.

pip install hypll

BibTeX

If you would like to cite this project, please use the following bibtex entry

@article{spengler2023hypll,
  title={HypLL: The Hyperbolic Learning Library},
  author={van Spengler, Max and Wirth, Philipp and Mettes, Pascal},
  journal={arXiv preprint arXiv:2306.06154},
  year={2023}
}