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Deep universal probabilistic programming with Python and PyTorch
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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 in a beta release. It is developed and maintained by Uber AI Labs and community contributors. For more information, check out our blog post.


Installing a stable Pyro release

Install using pip:

pip install pyro-ppl

Install from source:

git clone
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+

or, with the extras dependency to run the probabilistic models included in the examples/tutorials directories:

pip install git+[extras]

Install from source:

git clone
cd pyro
pip install .  # pip install .[extras] for running models in examples/tutorials

Running Pyro from a Docker Container

Refer to the instructions here.


If you use Pyro, please consider citing:

  author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and
            Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and
            Horsfall, Paul and Goodman, Noah D.},
  title = {{Pyro: Deep Universal Probabilistic Programming}},
  journal = {arXiv preprint arXiv:1810.09538},
  year = {2018}
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