flowMC

Normalizing-flow enhanced sampling package for probabilistic inference

Warning

As I have new priority in my life now, this code base is in very low maintanence mode. This means I am not actively developing the codebase, and only infrequently responds to the issue board. Small PRs that I can finish reviewing within say 15 minutes will still be looked at, but anything that changes more than 5-10 long files is unlikely to retain my attention. In the meantime, a group of friends of mine forked the repo here https://github.com/GW-JAX-Team/flowMC. I am not officially associated with them and have no idea on what their plan with the fork, but feel free to look over there to find if they have something you need.

Warning

Note that flowMC has not reached v1.0.0, meaning the API could subject to changes. In general, the higher level the API, the less likely it is going to change. However, intermediate level API such as the resource strategy interface could subject to major revision for performance concerns.

flowMC is a Jax-based python package for normalizing-flow enhanced Markov chain Monte Carlo (MCMC) sampling. The code is open source under MIT license, and it is under active development.

• Just-in-time compilation is supported.
• Native support for GPU acceleration.
• Suit for problems with multi-modality.
• Minimal tuning.

Installation

The simplest way to install the package is to do it through pip

pip install flowMC

This will install the latest stable release and its dependencies. flowMC is based on Jax and Equinox. By default, installing flowMC will automatically install Jax and Equinox available on PyPI. By default this install the CPU version of Jax. If you have a GPU and want to use it, you can install the GPU version of Jax by running:

pip install flowMC[cuda]

If you want to install the latest version of flowMC, you can clone this repo and install it locally:

git clone https://github.com/kazewong/flowMC.git
cd flowMC
pip install -e .

There are a couple more extras that you can install with flowMC, including:

• flowMC[docs]: Install the documentation dependencies.
• flowMC[codeqa]: Install the code quality dependencies.
• flowMC[visualize]: Install the visualization dependencies.

On top of pip installation, we highly encourage you to use uv to manage your python environment. Once you clone the repo, you can run uv sync to create a virtual environment with all the dependencies installed.

Attribution

If you used flowMC in your research, we would really appericiate it if you could at least cite the following papers:

@article{Wong:2022xvh,
    author = "Wong, Kaze W. k. and Gabri\'e, Marylou and Foreman-Mackey, Daniel",
    title = "{flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX}",
    eprint = "2211.06397",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.IM",
    doi = "10.21105/joss.05021",
    journal = "J. Open Source Softw.",
    volume = "8",
    number = "83",
    pages = "5021",
    year = "2023"
}

@article{Gabrie:2021tlu,
    author = "Gabri\'e, Marylou and Rotskoff, Grant M. and Vanden-Eijnden, Eric",
    title = "{Adaptive Monte Carlo augmented with normalizing flows}",
    eprint = "2105.12603",
    archivePrefix = "arXiv",
    primaryClass = "physics.data-an",
    doi = "10.1073/pnas.2109420119",
    journal = "Proc. Nat. Acad. Sci.",
    volume = "119",
    number = "10",
    pages = "e2109420119",
    year = "2022"
}

This will help flowMC getting more recognition, and the main benefit for you is this means the flowMC community will grow and it will be continuously improved. If you believe in the magic of open-source software, please support us by attributing our software in your work.

flowMC is a Jax implementation of methods described in:

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods Gabrié M., Rotskoff G. M., Vanden-Eijnden E. - ICML INNF+ workshop 2021 - pdf

Adaptive Monte Carlo augmented with normalizing flows. Gabrié M., Rotskoff G. M., Vanden-Eijnden E. - PNAS 2022 - doi, arxiv

Contributors

Hajime Kawahara 🐛	Daniel Dodd 📖 👀 ⚠️ 🐛	Matt Graham 🐛 ⚠️ 👀 📖	Kaze Wong 🐛 📝 💻 🖋 📖 💡 🚇 🚧 🔬 👀 ⚠️ ✅	Marylou Gabrié 🐛 💻 🖋 📖 💡 🚧 🔬 ⚠️ ✅	Meesum Qazalbash 💻 🚧	Thomas Ng 💻 🚧
Thomas Edwards 🐛 💻