A Python library for probabilistic modeling and inference
Project description
.. raw:: html
<div align="center">
.. raw:: html
</div>
--------------
`Getting Started <http://pyro.ai/examples>`__ \|
`Documentation <http://docs.pyro.ai/>`__ \|
`Community <http://forum.pyro.ai/>`__ \|
`Contributing <https://github.com/uber/pyro/blob/master/CONTRIBUTING.md>`__
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 <http://uber.ai>`__. For more information, check out our `blog
post <http://eng.uber.com/pyro>`__.
Installing
----------
Installing a stable Pyro release
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**Install using pip:**
.. code:: sh
pip install pyro-ppl
**Install from source:**
.. code:: sh
git clone git@github.com: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:
.. code:: sh
pip install pyro-ppl[extras]
Make sure that the models come from the same release version of the
`Pyro source code <https://github.com/uber/pyro/releases>`__ as you have
installed.
Installing Pyro dev branch
~~~~~~~~~~~~~~~~~~~~~~~~~~
For recent features you can install Pyro from source.
**Install using pip:**
.. code:: sh
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:
.. code:: sh
pip install git+https://github.com/uber/pyro.git#egg=project[extras]
**Install from source:**
.. code:: sh
git clone https://github.com/uber/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 <docker/README.md>`__.
Citation
--------
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}
}
.. |codecov.io| image:: https://codecov.io/github/uber/pyro/branch/dev/graph/badge.svg
:target: https://codecov.io/github/uber/pyro
<div align="center">
.. raw:: html
</div>
--------------
`Getting Started <http://pyro.ai/examples>`__ \|
`Documentation <http://docs.pyro.ai/>`__ \|
`Community <http://forum.pyro.ai/>`__ \|
`Contributing <https://github.com/uber/pyro/blob/master/CONTRIBUTING.md>`__
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 <http://uber.ai>`__. For more information, check out our `blog
post <http://eng.uber.com/pyro>`__.
Installing
----------
Installing a stable Pyro release
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**Install using pip:**
.. code:: sh
pip install pyro-ppl
**Install from source:**
.. code:: sh
git clone git@github.com: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:
.. code:: sh
pip install pyro-ppl[extras]
Make sure that the models come from the same release version of the
`Pyro source code <https://github.com/uber/pyro/releases>`__ as you have
installed.
Installing Pyro dev branch
~~~~~~~~~~~~~~~~~~~~~~~~~~
For recent features you can install Pyro from source.
**Install using pip:**
.. code:: sh
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:
.. code:: sh
pip install git+https://github.com/uber/pyro.git#egg=project[extras]
**Install from source:**
.. code:: sh
git clone https://github.com/uber/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 <docker/README.md>`__.
Citation
--------
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}
}
.. |codecov.io| image:: https://codecov.io/github/uber/pyro/branch/dev/graph/badge.svg
:target: https://codecov.io/github/uber/pyro
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