Metadata-Version: 2.1
Name: pyro-ppl
Version: 1.3.1
Summary: A Python library for probabilistic modeling and inference
Home-page: http://pyro.ai
Author: Uber AI Labs
Author-email: pyro@uber.com
License: Apache 2.0
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 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:**
        
        Pyro supports Python 3.4+.
        
        ```sh
        pip install pyro-ppl
        ```
        
        **Install from source:**
        ```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:
        ```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:**
        
        ```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:
        ```sh
        pip install git+https://github.com/pyro-ppl/pyro.git#egg=project[extras]
        ```
        
        **Install from source:**
        
        ```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{bingham2019pyro,
          author    = {Eli Bingham and
                       Jonathan P. Chen and
                       Martin Jankowiak and
                       Fritz Obermeyer and
                       Neeraj Pradhan and
                       Theofanis Karaletsos and
                       Rohit Singh and
                       Paul A. Szerlip and
                       Paul Horsfall and
                       Noah D. Goodman},
          title     = {Pyro: Deep Universal Probabilistic Programming},
          journal   = {J. Mach. Learn. Res.},
          volume    = {20},
          pages     = {28:1--28:6},
          year      = {2019},
          url       = {http://jmlr.org/papers/v20/18-403.html}
        }
        ```
        
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 :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: extras
Provides-Extra: test
Provides-Extra: profile
Provides-Extra: dev
