Deploy (these builds only succeed on tagged commits):
TorchANI is a pytorch implementation of ANI. It is currently under alpha release, which means, the API is not stable yet. If you find a bug of TorchANI, or have some feature request, feel free to open an issue on GitHub, or send us a pull request.
TorchANI requires the latest preview version of PyTorch. You can install PyTorch by the following commands (assuming cuda10):
pip install numpy pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html
If you updated TorchANI, you may also need to update PyTorch:
pip install --upgrade --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html
After installing the correct PyTorch, you can install TorchANI by:
pip install torchani
See also PyTorch's official site for instructions of installing latest preview version of PyTorch.
Please install nightly PyTorch through
pip install instead of
conda install. If your PyTorch is installed through
conda install, then
pip would mistakenly recognize the package name as
torch instead of
torch-nightly, which would cause dependency issue when installing TorchANI.
To run the tests and examples, you must manually download a data package
The original ANI-1 paper is:
- Smith JS, Isayev O, Roitberg AE. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical science. 2017;8(4):3192-203.
We are planning a seperate paper for TorchANI, it will be available when we are ready for beta release of TorchANI.
See also: isayev/ASE_ANI
To install TorchANI from GitHub:
git clone https://github.com/aiqm/torchani.git cd torchani pip install -e .
After TorchANI has been installed, you can build the documents by running
sphinx-build docs build. But make sure you
pip install sphinx sphinx-gallery pillow matplotlib sphinx_rtd_theme
To manually run unit tests, do
python setup.py nosetests
Note to TorchANI developers
Never commit to the master branch directly. If you need to change something, create a new branch, submit a PR on GitHub.
You must pass all the tests on GitHub before your PR can be merged.
Code review is required before merging pull request.