jpchen / beanmachine-1

A library that allows for inference on probabilistic models

Bean Machine

Overview

Bean Machine is a probabilistic programming language for inference over statistical models written in the Python language using a declarative syntax. Bean Machine is built on top of PyTorch and Bean Machine Graph, a custom C++ backend. Check out our tutorials and Quick Start to get started!

Installation

Bean Machine supports Python 3.7-3.10 and PyTorch 1.12.

Install the Latest Release with Pip

python -m pip install beanmachine

Install from Source

To download the latest Bean Machine source code from GitHub:

git clone https://github.com/facebookresearch/beanmachine.git
cd beanmachine

Then, you can choose from any of the following installation options.

Package Managers (Anaconda and Vcpkg)

Installing Bean Machine from source requires three external dependencies: Boost, Eigen, and range-v3.

For installing Boost and Eigen, we recommend using conda to manage the virtual environment and install the necessary build dependencies.

conda create -n {env name} python=3.8; conda activate {env name}
conda install -c conda-forge boost-cpp eigen=3.4.0

There are multiple ways of installing range-v3, including through vcpkg:

git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh
./vcpkg integrate install
./vcpkg install range-v3

Once dependencies are installed, install Bean Machine by running Pip:

python -m pip install .

Docker

docker build -t beanmachine .
docker run -it beanmachine:latest bash

Validate Installation

If you would like to run the builtin unit tests:

python -m pip install "beanmachine[test]"
pytest .

License

Bean Machine is MIT licensed, as found in the LICENSE file.