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* Implement OneTwoMatching.enumerate_support()

* Clarify docs

* Sketch BP computation of Bethe free energy

* Add large-scale smoke test

* Fix some bugs

* Fix sign error

* Simplify

* Add float32 tests

* Clarify; add debug statements

* Relax test, clamp logits

* Optimize clamping logic

* Simplify Z2 computation

* Avoid splitting logits in half

* Add naive sampling algorithm

* Add a more accurate mean field algorithm

* Fix tests

* Simplify

* Change temperature, tighten tests

* Remove unused algorithms

* Update docs and comments

* Numerically stabilize

* Improve accuraccy

* Remove heuristic

* Reduce memory footprint

* Improve accuracy of free energy formula

* Fuse ops

* Implement OneTwoMatching.mode()

* Simplify

* Simplify test

* Simplify and add a test

* Fix docs

* Add Sinkhorn preconditioner

* Add a .mode() method using lap

* Fix in-place op

* Implement .sample() via perturb-and-map

* Remove bad .sample() implementation

* Add link to docs

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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 was originally developed at Uber AI and is now actively maintained by community contributors, including a dedicated team at the Broad Institute. In 2019, Pyro became a project of the Linux Foundation, a neutral space for collaboration on open source software, open standards, open data, and open hardware.

For more information about the high level motivation for Pyro, check out our launch blog post. For additional blog posts, check out work on experimental design and time-to-event modeling in Pyro.


Installing a stable Pyro release

Install using pip:

Pyro supports Python 3.6+.

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    = {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       = {}
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