Pyro, an LF AI Foundation Incubation Project, has released version 1.3 and we’re thrilled to see another great release from the community. 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.
In version 1.3, Pyro adds a variety of improvements, including:
- A forecasting module for multivariate hierarchical heavy-tailed time series
- An AutoNormalizingFlow guide
- Subsample-compatible AutoNormal and AutoDelta guides
- A pyro.subsample primitive
- Four new tutorials
- A NeuTra example
- And more…
To learn more about the Pyro 1.3 release, check out the full release notes. Want to get involved with Pyro? Be sure to join the Pyro Announce and Pyro Technical-Discuss mailing lists to join the community and stay connected on the latest updates.
Congratulations to the Pyro team and we look forward to continued growth and success as part of the LF AI Foundation! To learn about hosting an open source project with us, visit the LF AI Foundation website.
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