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<!DOCTYPE html>
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<head>
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<meta name="description" content="Deep Universal Probabilistic Programming">
<title>Pyro</title>
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<br>
<h1>Pyro</h1>
<hr>
<p style="color: #EEE; opacity: .95;">Deep Universal Probabilistic Programming</p>
<a href="#install" class="btn btn-primary btn-xl page-scroll">Install</a>
<a href="http://docs.pyro.ai" class="btn btn-primary btn-xl page-scroll">Docs</a>
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<h2 class="section-heading text-center">About Pyro</h2>
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<p class="text-faded"><strong>NumPyro Release</strong> We’re excited to announce the release of <a href="https://github.com/pyro-ppl/numpyro">NumPyro</a>, a NumPy-backed Pyro using <a href="https://github.com/google/jax">JAX</a> for automatic differentiation and JIT compilation, with over 100x speedup for HMC and NUTS! See the <a href="http://pyro.ai/numpyro">examples</a> and <a href="https://num.pyro.ai">documentation</a> for more details.
</p>
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<p class="text-faded">
Pyro is a universal probabilistic programming language (PPL) written in Python and supported by <a href="http://pytorch.org">PyTorch</a> on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with these key principles:<br> <br>
<b>Universal</b>: Pyro can represent any computable probability distribution.<br>
<b>Scalable</b>: Pyro scales to large data sets with little overhead.<br>
<b>Minimal</b>: Pyro is implemented with a small core of powerful, composable abstractions.<br>
<b>Flexible</b>: Pyro aims for automation when you want it, control when you need it. <br>
<br> <br>
Check out the <a href="https://eng.uber.com/pyro">blog post</a> for more background or dive into the <a href="examples/">tutorials</a>.
</p>
<br>
<!--<p class="text-faded text-center" style="font-size: 20px"> <i>Pyro is in a Beta release.</i> </p>-->
<!--a href="https://arxiv.org/pdf/" class="btn btn-default btn-xl">Read the Paper</a-->
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<h2 class="section-heading">How to Install Pyro</h2>
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<p>Pyro supports Python 3. <br>
<h4 class="section-heading">Install via Pip</h4>
First install <a href="http://pytorch.org">PyTorch</a>. Then install Pyro via pip:</p>
<p class="text-faded"><pre><code class="language-shell"> pip3 install pyro-ppl </code></pre></p>
<h4 class="section-heading">Install from source</h4>
<p class="text-faded"><pre><code class="language-shell">git clone https://github.com/pyro-ppl/pyro.git
cd pyro
pip install .[extras]</code></pre></p>
<h4 class="section-heading">Running Docker Image</h4>
Follow the instructions <a href="https://github.com/pyro-ppl/pyro/blob/dev/docker/README.md">here</a>.</p>
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<h2>Pyro is an <a href="https://github.com/pyro-ppl/pyro/blob/dev/LICENSE.md">Apache 2.0</a>-Licensed Open Source Project</h2>
<p> If you use Pyro or NumPyro in your research, please consider citing our papers.</p>
<br>
<div class="col-lg-8 col-lg-offset-2 text-left">
<pre>
@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 = {Journal of Machine Learning Research},
year = {2018}
}
@article{phan2019composable,
author = {Phan, Du and Pradhan, Neeraj and Jankowiak, Martin},
title = {Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro},
journal = {arXiv preprint arXiv:1912.11554},
year = {2019}
}
</pre>
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<h2 class="section-heading">Institutions Using Pyro:</h2>
<p>(Add yours too!)</p>
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