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 is in an alpha release. It is developed and used by Uber AI Labs. For more information, check out our blog post.