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pyro

> 编程语言
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使用 Python 和 PyTorch 进行深度通用概率编程

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工具介绍

使用 Python 和 PyTorch 进行深度通用概率编程


Getting Started | Documentation | Community | Contributing

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. In 2019, Pyro became 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. For additional blog posts, check out work on experimental design and time-to-event modeling in Pyro.

Installing

Installing a stable Pyro release

Install using pip:

bash
pip install pyro-ppl

Install from source:

bash
git clone [email protected]: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:

bash
pip install pyro-ppl[extras] 

Make sure that the models come from the same release version of the Pyro source code as you have installed.

Installing Pyro dev branch

For recent features you can install Pyro from source.

Install Pyro using pip:

bash
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:

bash
pip install git+https://github.com/pyro-ppl/pyro.git#egg=project[extras]

Install Pyro from source:

bash
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.

Citation

If you use Pyro, please consider citing:

…

Issues· 291 开放

查看全部 Issues在 GitHub 打开
  • #3468

    [错误] AutoGuide 静默地覆盖模型的手动 `pyro.plate(subsample=idx)`

    更新于 2026年8月12日
  • #3368

    使用分类分布时没有后向样本 [错误]

    bug更新于 2026年7月23日
  • #3419

    [错误] 双项分布的批量混合无法通过验证

    bug更新于 2026年7月23日
  • #2201

    在 tests/test_examples.py 中启用警告

    help wantedwarnings & errors更新于 2026年7月23日
  • #3450

    [错误] 无效的逃逸序列

    更新于 2026年7月1日
  • #3449

    [错误] 在运行 NUTS 且 jit_compile=True 时,subsample_messenger 会产生 TracerWarning

    更新于 2026年6月29日
  • #3417

    向 spline_coupling 帮助函数添加订单参数

    help wantedusability更新于 2026年3月31日
  • #1461

    征集用户贡献的示例和教程!

    help wanteddocumentationExamplesgood first issue更新于 2025年12月29日
  • #3448

    [功能请求] 新分布: 逆威尔特分布

    更新于 2025年12月1日
  • #3447

    [功能请求] 新的 Pyro 版本是否要更新数据集 URL?

    更新于 2025年11月5日

> 标签

Pythonbayesianbayesian-inferencedeep-learningmachine-learning

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> 工具信息

发布日期2026年8月1日
最后更新2026年9月19日
分类编程语言
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