Metadata-Version: 2.1
Name: pyro-ppl
Version: 0.3.1
Summary: A Python library for probabilistic modeling and inference
Home-page: http://pyro.ai
Author: Uber AI Labs
Author-email: pyro@uber.com
License: MIT License
Description: `Getting Started <http://pyro.ai/examples>`__ \|
        `Documentation <http://docs.pyro.ai/>`__ \|
        `Community <http://forum.pyro.ai/>`__ \|
        `Contributing <https://github.com/pyro-ppl/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 a beta release. It is developed and maintained by `Uber AI
        Labs <http://uber.ai>`__ and community contributors. 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:pyro-ppl/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/pyro-ppl/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/pyro-ppl/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/pyro-ppl/pyro.git#egg=project[extras]
        
        **Install from source:**
        
        .. code:: sh
        
            git clone https://github.com/pyro-ppl/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}
            }
        
Keywords: machine learning statistics probabilistic programming bayesian modeling pytorch
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Provides-Extra: test
Provides-Extra: profile
Provides-Extra: extras
Provides-Extra: dev
