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A Python library for probabilistic modeling and inference

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`Getting Started <http://pyro.ai/examples>`__ \|
`Documentation <http://docs.pyro.ai/>`__ \|
`Contributing <https://github.com/uber/pyro/blob/master/CONTRIBUTING.md>`__

Please also refer to the `Pyro homepage <http://pyro.ai/>`__.

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>`__. More information is available in the `launch
blog post <http://eng.uber.com/pyro>`__.

Installation
------------

First install `PyTorch <http://pytorch.org/>`__.

Install via pip:

**Python 2.7.*:**

.. code:: sh

pip install pyro-ppl

**Python 3.5:**

::

pip3 install pyro-ppl

**Install from source:**

.. code:: sh

git clone git@github.com:uber/pyro.git
cd pyro
pip install .

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