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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 an alpha release. It is developed and used by Uber AI Labs. For more information, check out our blog post.

First install PyTorch.

Install via pip:

Python 2.7.*:

pip install pyro-ppl

Python 3.5:

pip3 install pyro-ppl

Install from source:

git clone [email protected]:uber/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.

For recent features you can install Pyro from source.

To install a compatible CPU version of PyTorch on OSX / Linux, you could use the PyTorch install helper script.

bash scripts/install_pytorch.sh

Alternatively, build PyTorch following instructions in the PyTorch README.

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
git checkout 200fb22  # <---- a well-tested commit

On Linux:

python setup.py install

On OSX:

MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install

Finally install Pyro using pip or from source as shown below.

Install using pip:

pip install git+https://github.com/uber/pyro.git

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

pip install git+https://github.com/uber/pyro.git#egg=project[extras]

Install from source:

git clone https://github.com/uber/pyro
cd pyro
pip install .  # pip install .[extras] for running models in examples/tutorials

To use Pyro features that are under active development and only available with the PyTorch's forthcoming 1.0 release e.g. JIT compilation, you will need to use the pytorch-1.0 branch of Pyro.

First install the PyTorch release candidate using the Preview tab from the PyTorch website. Alternatively, you could build PyTorch following instructions in the PyTorch README.

Then, install Pyro using the pytorch-1.0 branch.

Install using pip:

pip install git+https://github.com/uber/[email protected]

or, with the extras dependency as shown above.

Install from source:

git clone https://github.com/uber/pyro
cd pyro
git checkout pytorch-1.0  # branch compatible with PyTorch 1.0 release candidate
pip install .  # pip install .[extras] for running models in examples/tutorials

Refer to the instructions here.

If you use Pyro, please consider citing:

@article{bingham2018pyro,
  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}
}