Welcome to Cheetah’s documentation!¶
Cheetah is a particle tracking accelerator we built specifically to speed up the training of reinforcement learning models.
GitHub repository: https://github.com/desy-ml/cheetah
Paper: https://arxiv.org/abs/2401.05815
Installation¶
Simply install Cheetah from PyPI by running the following command.
pip install cheetah-accelerator
Examples¶
We provide some examples to demonstrate some features of Cheetah and show how to use them. They provide a good entry point to using Cheetah, but they do not represent its full functionality. To move beyond the examples, please refer to the in-depth documentation. If you feel like other examples should be added, feel free to open an issue on GitHub.
Documentation¶
For more advanced usage, please refer to the in-depth documentation.
Documentation
Cite Cheetah¶
If you use Cheetah, please cite the following two papers:
@misc{kaiser2024cheetah,
title = {Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations},
author = {Kaiser, Jan and Xu, Chenran and Eichler, Annika and {Santamaria Garcia}, Andrea},
year = {2024},
eprint = {2401.05815},
archiveprefix = {arXiv},
primaryclass = {physics.acc-ph}
}
@inproceedings{stein2022accelerating,
title = {Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications},
author = {Stein, Oliver and Kaiser, Jan and Eichler, Annika},
year = {2022},
booktitle = {Proceedings of the 13th International Particle Accelerator Conference}
}
For Developers¶
Activate your virtual environment. (Optional)
Install the cheetah package as editable
pip install -e .
We suggest installing pre-commit hooks to automatically conform with the code formatting in commits:
pip install pre-commit
pre-commit install
