Pyro
A Python library for deep probabilistic programming, built on PyTorch, for building complex statistical models.
| What is it | A Python library for deep probabilistic programming, built on PyTorch, for building complex statistical models. |
|---|---|
| Pricing | Unknown |
| Platform | API |
| API | Yes |
| Best for | building probabilistic models, academic research in Bayesian statistics |
| Domain registered | 2017 |
Data updated Aug. 1, 2026
What does Pyro do?
Pyro is a probabilistic programming language built in Python that uses PyTorch as its backend. It is designed for creating sophisticated models that incorporate uncertainty, which is essential for tasks like forecasting, anomaly detection, and interpreting complex data. Instead of writing complex statistical code from scratch, you define models using Pyro's abstractions, and it handles the intricate computations needed for Bayesian inference. This makes it possible to build and test models that would otherwise be extremely difficult to implement.
The tool stands out through its core design principles: it is universal, meaning it can represent any computable probability distribution; scalable for handling large datasets; minimal with a small set of powerful abstractions; and flexible, offering automation for common tasks while giving you control for custom work. A related project, NumPyro, offers a massive performance boost by using JAX for automatic differentiation and just-in-time compilation, providing over 100x speedup for certain algorithms.
Pyro is primarily for data scientists, researchers, and machine learning engineers who are working on projects that require probabilistic reasoning. This includes academic research in fields like epidemiology or physics, developing AI systems that need to quantify uncertainty, and building industrial applications for risk assessment or recommendation systems that benefit from a Bayesian approach.
Key features
What makes it stand outWho is Pyro for?
Who benefits most from this toolTrust & presence
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