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/>`__ \|
`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 .
<div align="center">
.. raw:: html
</div>
--------------
`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 .
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pyro-ppl-0.1.2.tar.gz
(71.4 kB
view hashes)