Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.
Project description
e3nn-jax
:rocket: 44% faster than pytorch*
*Speed comparison done with a full model (MACE) during training (revMD-17) on a GPU (NVIDIA RTX A5000)
Documentation
Please always check the ChangeLog for breaking changes.
Installation
To install the latest released version:
pip install --upgrade e3nn-jax
To install the latest GitHub version:
pip install git+https://github.com/e3nn/e3nn-jax.git
Need Help?
Ask a question in the discussions tab.
What is different from the PyTorch version?
The main difference is the presence of the class IrrepsArray
.
IrrepsArray
contains the irreps (Irreps
) along with the data array.
Citing
@misc{e3nn_paper,
doi = {10.48550/ARXIV.2207.09453},
url = {https://arxiv.org/abs/2207.09453},
author = {Geiger, Mario and Smidt, Tess},
keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {e3nn: Euclidean Neural Networks},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
e3nn_jax-0.17.4.tar.gz
(118.7 kB
view hashes)
Built Distribution
e3nn_jax-0.17.4-py3-none-any.whl
(148.6 kB
view hashes)
Close
Hashes for e3nn_jax-0.17.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8e882c9dac09878c82caf2e347e1771eb78ed5aae6972b3cdb84019236fd5b61 |
|
MD5 | 16fbb2842440337b2695d1468eb50ee6 |
|
BLAKE2b-256 | 1662c12638dc8beb23393cc256d9f0301af0433b4205ed8525e939e1b0132831 |