# JaxPM [](https://colab.research.google.com/github/DifferentiableUniverseInitiative/JaxPM/blob/main/notebooks/01-Introduction.ipynb) [](https://pypi.org/project/jaxpm/) [](https://github.com/DifferentiableUniverseInitiative/JaxPM/actions/workflows/tests.yml) [](#contributors-) JAX-powered Cosmological Particle-Mesh N-body Solver > ### Note > **The new JaxPM v0.1.xx** supports multi-GPU model distribution while remaining compatible with previous releases. These significant changes are still under development and testing, so please report any issues you encounter. > For the older but more stable version, install: > ```bash > pip install jaxpm==0.0.2 > ``` ## Install Basic installation can be done using pip: ```bash pip install jaxpm ``` For more advanced installation for optimized distribution on gpu clusters, please install jaxDecomp first. See instructions [here](https://github.com/DifferentiableUniverseInitiative/jaxDecomp). ## Goals Provide a modern infrastructure to support differentiable PM N-body simulations using JAX: - Keep implementation simple and readable, in pure NumPy API - Any order forward and backward automatic differentiation - Support automated batching using `vmap` - Compatibility with external optimizer libraries like `optax` - Now fully distributable on **multi-GPU and multi-node** systems using [jaxDecomp](https://github.com/DifferentiableUniverseInitiative/jaxDecomp) working with`JAX v0.4.35` ## Open development and use Current expectations are: - This project is and will remain open source, and usable without any restrictions for any purposes - Will be a simple publication on [The Journal of Open Source Software](https://joss.theoj.org/) - Everyone is welcome to contribute, and can join the JOSS publication (until it is submitted to the journal). - Anyone (including main contributors) can use this code as a framework to build and publish their own applications, with no expectation that they *need* to extend authorship to all jaxpm developers. ## Getting Started To dive into JaxPM’s capabilities, please explore the **notebook section** for detailed tutorials and examples on various setups, from single-device simulations to multi-host configurations. You can find the notebooks' [README here](notebooks/README.md) for a structured guide through each tutorial. ## Contributors ✨ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
Francois Lanusse 🤔 |
Denise Lanzieri 💻 |
Wassim KABALAN 💻 🚇 👀 |
Hugo Simon-Onfroy 💻 |
Alexandre Boucaud 👀 |