[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 was originally developed at Uber AI and is now actively maintained by community contributors, including a dedicated team at the [Broad Institute](https://www.broadinstitute.org/).
In 2019, Pyro [became](https://www.linuxfoundation.org/press-release/2019/02/pyro-probabilistic-programming-language-becomes-newest-lf-deep-learning-project/) a project of the Linux Foundation, a neutral space for collaboration on open source software, open standards, open data, and open hardware.
For more information about the high level motivation for Pyro, check out our [launch blog post](http://eng.uber.com/pyro).
For additional blog posts, check out work on [experimental design](https://eng.uber.com/oed-pyro-release/) and
[time-to-event modeling](https://eng.uber.com/modeling-censored-time-to-event-data-using-pyro/) in Pyro.
## Installing
### Installing a stable Pyro release
**Install using pip:**
```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 Pyro 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 Pyro 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}
}
```
Raw data
{
"_id": null,
"home_page": "http://pyro.ai",
"name": "pyro-ppl",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "machine learning statistics probabilistic programming bayesian modeling pytorch",
"author": "Uber AI Labs",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/4c/2e/3bcba8688d58f8dc954cef6831c19d52b6017b035d783685d67cd99fa351/pyro_ppl-1.9.1.tar.gz",
"platform": null,
"description": "[Getting Started](http://pyro.ai/examples) |\n[Documentation](http://docs.pyro.ai/) |\n[Community](http://forum.pyro.ai/) |\n[Contributing](https://github.com/pyro-ppl/pyro/blob/master/CONTRIBUTING.md)\n\nPyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:\n\n- **Universal**: Pyro is a universal PPL - it can represent any computable probability distribution.\n- **Scalable**: Pyro scales to large data sets with little overhead compared to hand-written code.\n- **Minimal**: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.\n- **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.\n\nPyro was originally developed at Uber AI and is now actively maintained by community contributors, including a dedicated team at the [Broad Institute](https://www.broadinstitute.org/).\nIn 2019, Pyro [became](https://www.linuxfoundation.org/press-release/2019/02/pyro-probabilistic-programming-language-becomes-newest-lf-deep-learning-project/) a project of the Linux Foundation, a neutral space for collaboration on open source software, open standards, open data, and open hardware.\n\nFor more information about the high level motivation for Pyro, check out our [launch blog post](http://eng.uber.com/pyro).\nFor additional blog posts, check out work on [experimental design](https://eng.uber.com/oed-pyro-release/) and\n[time-to-event modeling](https://eng.uber.com/modeling-censored-time-to-event-data-using-pyro/) in Pyro.\n\n## Installing\n\n### Installing a stable Pyro release\n\n**Install using pip:**\n```sh\npip install pyro-ppl\n```\n\n**Install from source:**\n```sh\ngit clone git@github.com:pyro-ppl/pyro.git\ncd pyro\ngit checkout master # master is pinned to the latest release\npip install .\n```\n\n**Install with extra packages:**\n\nTo install the dependencies required to run the probabilistic models included in the `examples`/`tutorials` directories, please use the following command:\n```sh\npip install pyro-ppl[extras] \n```\nMake 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.\n\n### Installing Pyro dev branch\n\nFor recent features you can install Pyro from source.\n\n**Install Pyro using pip:**\n\n```sh\npip install git+https://github.com/pyro-ppl/pyro.git\n```\nor, with the `extras` dependency to run the probabilistic models included in the `examples`/`tutorials` directories:\n```sh\npip install git+https://github.com/pyro-ppl/pyro.git#egg=project[extras]\n```\n\n**Install Pyro from source:**\n\n```sh\ngit clone https://github.com/pyro-ppl/pyro\ncd pyro\npip install . # pip install .[extras] for running models in examples/tutorials\n```\n\n## Running Pyro from a Docker Container\n\nRefer to the instructions [here](docker/README.md).\n\n## Citation\nIf you use Pyro, please consider citing:\n```\n@article{bingham2019pyro,\n author = {Eli Bingham and\n Jonathan P. Chen and\n Martin Jankowiak and\n Fritz Obermeyer and\n Neeraj Pradhan and\n Theofanis Karaletsos and\n Rohit Singh and\n Paul A. Szerlip and\n Paul Horsfall and\n Noah D. Goodman},\n title = {Pyro: Deep Universal Probabilistic Programming},\n journal = {J. Mach. Learn. Res.},\n volume = {20},\n pages = {28:1--28:6},\n year = {2019},\n url = {http://jmlr.org/papers/v20/18-403.html}\n}\n```\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "A Python library for probabilistic modeling and inference",
"version": "1.9.1",
"project_urls": {
"Documentation": "https://docs.pyro.ai",
"Homepage": "http://pyro.ai",
"Source": "https://github.com/pyro-ppl/pyro"
},
"split_keywords": [
"machine",
"learning",
"statistics",
"probabilistic",
"programming",
"bayesian",
"modeling",
"pytorch"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ed37def183a2a2c8619d92649d62fe0622c4c6c62f60e4151e8fbaa409e7d5ab",
"md5": "7145c449573a3f90788aecbc7e08c7da",
"sha256": "91fb2c8740d9d3bd548180ac5ecfa04552ed8c471a1ab66870180663b8f09852"
},
"downloads": -1,
"filename": "pyro_ppl-1.9.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7145c449573a3f90788aecbc7e08c7da",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 755956,
"upload_time": "2024-06-02T00:37:37",
"upload_time_iso_8601": "2024-06-02T00:37:37.486782Z",
"url": "https://files.pythonhosted.org/packages/ed/37/def183a2a2c8619d92649d62fe0622c4c6c62f60e4151e8fbaa409e7d5ab/pyro_ppl-1.9.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4c2e3bcba8688d58f8dc954cef6831c19d52b6017b035d783685d67cd99fa351",
"md5": "7d545de32aeb4e4769ac2555dd36f0d0",
"sha256": "5e1596de276c038a3f77d2580a90d0a97126e0104900444a088eee620bb0d65e"
},
"downloads": -1,
"filename": "pyro_ppl-1.9.1.tar.gz",
"has_sig": false,
"md5_digest": "7d545de32aeb4e4769ac2555dd36f0d0",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 570861,
"upload_time": "2024-06-02T00:37:39",
"upload_time_iso_8601": "2024-06-02T00:37:39.688176Z",
"url": "https://files.pythonhosted.org/packages/4c/2e/3bcba8688d58f8dc954cef6831c19d52b6017b035d783685d67cd99fa351/pyro_ppl-1.9.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-06-02 00:37:39",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "pyro-ppl",
"github_project": "pyro",
"travis_ci": false,
"coveralls": true,
"github_actions": true,
"lcname": "pyro-ppl"
}
JSON
