About Pyro

NumPyro Release We’re excited to announce the release of NumPyro, a NumPy-backed Pyro using JAX for automatic differentiation and JIT compilation, with over 100x speedup for HMC and NUTS! See the examples and documentation for more details.

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with these key principles:



Universal: Pyro can represent any computable probability distribution.

Scalable: Pyro scales to large data sets with little overhead.

Minimal: Pyro is implemented with a small core of powerful, composable abstractions.

Flexible: Pyro aims for automation when you want it, control when you need it.





Check out the blog post for more background or dive into the tutorials.