Getting Started, Documentation, Community, Contributing
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:
- Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
- Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference.
Pyro is developed and maintained by Uber AI Labs and community contributors.
For more information, check out our blog post.
Installing
Installing a stable Pyro release
Install using pip:
Pyro supports Python 3.4+.
pip install pyro-ppl
Install from source:
git clone git@github.com:pyro-ppl/pyro.git
cd pyro
git checkout master # master is pinned to the latest release
pip install .
Install with extra packages:
To install the dependencies required to run the probabilistic models included in the examples/tutorials directories, please use the following command:
pip install pyro-ppl[extras]
Make sure that the models come from the same release version of the Pyro source code as you have installed.
Installing Pyro dev branch
For recent features you can install Pyro from source.
Install using pip:
pip install git+https://github.com/pyro-ppl/pyro.git
or, with the extras dependency to run the probabilistic models included in the examples/tutorials directories:
pip install git+https://github.com/pyro-ppl/pyro.git#egg=project[extras]
Install from source:
git clone https://github.com/pyro-ppl/pyro
cd pyro
pip install . # pip install .[extras] for running models in examples/tutorials
Running Pyro from a Docker Container
Refer to the instructions here.
Citation
If you use Pyro, please consider citing:
@article{bingham2019pyro,
author = {Eli Bingham and
Jonathan P. Chen and
Martin Jankowiak and
Fritz Obermeyer and
Neeraj Pradhan and
Theofanis Karaletsos and
Rohit Singh and
Paul A. Szerlip and
Paul Horsfall and
Noah D. Goodman},
title = {Pyro: Deep Universal Probabilistic Programming},
journal = {J. Mach. Learn. Res.},
volume = {20},
pages = {28:1--28:6},
year = {2019},
url = {http://jmlr.org/papers/v20/18-403.html}
}
