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.
Getting Started¶
These pages explain how to get started with Cheetah.
Documentation¶
For more advanced usage, please refer to the in-depth documentation.
Cite Cheetah¶
If you use Cheetah, please cite the following two papers:
@article{kaiser2024cheetah,
title = {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,
month = {May},
journal = {Phys. Rev. Accel. Beams},
publisher = {American Physical Society},
volume = 27,
pages = {054601},
doi = {10.1103/PhysRevAccelBeams.27.054601},
url = {https://link.aps.org/doi/10.1103/PhysRevAccelBeams.27.054601},
issue = 5,
numpages = 17
}
@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