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Pyro brings probabilistic programming capabilities to Python projects using PyTorch.
About pyro
Pyro is an open source probabilistic programming library for Python built on PyTorch. It supports representation of computable probability distributions and provides abstractions to express generative and inference models, with automated inference and options for experts to customize inference.
It can be installed with pip, from source, or run from a Docker container. An extras install is required to run the models in the examples and tutorials directories. It was originally developed at Uber AI and is now maintained by community contributors, including a team at the Broad Institute, and became a Linux Foundation project in 2019.
Key features
- Represent computable probability distributions in Python
- Express generative and inference models with abstractions
- Allow experts to customize inference methods
- Install stable release with pip install pyro-ppl
- Install optional extras to run example and tutorial models
- Run from a Docker container
Best for: Python developers using PyTorch who build probabilistic models and need control over inference.
Good for
- Open source under Apache-2.0 license
- Built on PyTorch for Python projects
- Installable via pip, source, or Docker container
pyro at a glance
- Website
- github.com/pyro-ppl/pyro
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Linux
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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pyro FAQ
How do I install a stable Pyro release?
Run pip install pyro-ppl, or clone the repository and run pip install .
How do I run the models in the examples and tutorials directories?
Install with pip install pyro-ppl[extras] and use models from the same release version as the installed code.
How do I install the development version?
Run pip install git+https://github.com/pyro-ppl/pyro.git, with [extras] if you need to run example models.
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