CoDeX contains learned data compression tools for JAX.
You can use this library to build your own ML models with end-to-end optimized data compression built in. It's useful to find storage-efficient representations of your data (images, features, examples, etc.) while only sacrificing a small fraction of model performance.
For a more in-depth introduction from a classical data compression perspective, consider our paper on nonlinear transform coding, or watch @jonaballe's talk on learned image compression. For an introduction to lossy data compression from a machine learning perspective, take a look at @yiboyang's review paper.
Please post all questions or comments on Discussions. Only file Issues for actual bugs or feature requests. On Discussions, you may get a faster answer, and you help other people find the question or answer more easily later.
To install CoDeX via pip, run the following command:
python -m pip install jax-codexTo test that the installation works correctly, you can run the unit tests with:
python -m pip install pytest chex tensorflow-probability
pytest --pyargs codexOnce the command finishes, you should see a message 13 passed in 2.76s or
similar in the last line.
We recommend importing the library from your Python code as follows:
import codex as cdxIf you use this library for research purposes, please cite:
@software{codex_github,
author = "Ballé, Jona and Hwang, Sung Jin and Agustsson, Eirikur",
title = "{CoDeX}: Learned Data Compression in {JAX}",
url = "http://github.com/balle-lab/codex",
version = "0.0.1",
year = "2022",
}
In the above BibTeX entry, names are top contributors sorted by number of commits. Please adjust version number and year according to the version that was actually used.