# beanmachine

**Repository Path**: facebookresearch/beanmachine

## Basic Information

- **Project Name**: beanmachine
- **Description**: A library that allows for inference on probabilistic models
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: agg-dtools
- **Homepage**: https://beanmachine.org/
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-07-24
- **Last Updated**: 2023-08-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Bean Machine
<div align="center">
  <a href="http://beanmachine.org"> <img width="220px" height="220px" src="https://beanmachine.org/img/beanmachine.svg"></a>
</div>

[![Lint](https://github.com/facebookresearch/beanmachine/actions/workflows/lint.yml/badge.svg)](https://github.com/facebookresearch/beanmachine/actions/workflows/lint.yml)
[![Tests](https://github.com/facebookresearch/beanmachine/actions/workflows/test.yml/badge.svg)](https://github.com/facebookresearch/beanmachine/actions/workflows/test.yml)
[![PyPI](https://img.shields.io/pypi/v/beanmachine)](https://pypi.org/project/beanmachine)


## 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](https://beanmachine.org/docs/overview/tutorials/Coin_flipping/CoinFlipping/) and [Quick Start](https://beanmachine.org/docs/overview/quick_start/) to get started!

## Installation
Bean Machine supports Python 3.7-3.10 and PyTorch 1.12.

### Install the Latest Release with Pip

```bash
python -m pip install beanmachine
```

### Install from Source

To download the latest Bean Machine source code from GitHub:

```bash
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](https://www.boost.org/), [Eigen](https://eigen.tuxfamily.org/index.php?title=Main_Page), and [`range-v3`](https://github.com/ericniebler/range-v3).

For installing Boost and Eigen, we recommend using [conda](https://docs.conda.io/en/latest/) to manage the virtual environment and install the necessary build dependencies.

```bash
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`](https://github.com/ericniebler/range-v3#building-range-v3---using-vcpkg), including through [`vcpkg`](https://github.com/Microsoft/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

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

#### Validate Installation

If you would like to run the builtin unit tests:

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

## License
Bean Machine is MIT licensed, as found in the [LICENSE](LICENSE) file.