Contributed by Uber, Pyro Enables Flexible and Expressive Deep Probabilistic Modeling
The LF Deep Learning Foundation (LF DL), a Linux Foundation project that supports and sustains open source innovation in artificial intelligence (AI), machine learning (ML), and deep learning (DL), announces the Pyro project, started by Uber, as its newest incubation project. Built on top of the PyTorch framework, Pyro is a deep probabilistic programming framework that facilitates large-scale exploration of AI models, making deep learning model development and testing quicker and more seamless. This is the second project LF DL has voted in from Uber, following last December’s Horovod announcement.
Pyro is used by large companies like Siemens, IBM, and Uber, and startups like Noodle.AI, in addition to Harvard University, MIT, Stanford University, University of Oxford, University of Cambridge, and The Broad Institute. At Uber, Pyro solves a range of problems including sensor fusion, time series forecasting, ad campaign optimization and data augmentation for deep image understanding.
Pyro is the fifth project to join LF DL, which provides financial and intellectual resources, infrastructure, marketing, research, creative services, and events support. This rich neutral environment spurs the rapid advancement of its projects, including Acumos AI, the Angel project, EDL project and Horovod, by encouraging additional contributors as well as broader collaboration across the open source community.
“The LF Deep Learning Foundation is excited to welcome Pyro to our family of projects. Today’s announcement of Uber’s contribution of the project brings us closer to our goal of building a comprehensive ecosystem of AI, machine learning and deep learning project,” said Ibrahim Haddad, Executive Director of the LF DL. “We look forward to helping to grow the community contributing to and using Pyro to further improve forecasting and other capabilities.”
Pyro was designed with four key principles in mind:
- 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.
“Pyro was originally created at Uber AI Labs to help make deep probabilistic programming faster and more seamless for AI practitioners in both industry and academia,” said Zoubin Ghahramani, head of Uber AI Labs. “By incorporating Pyro into the LF DL portfolio, we hope to facilitate greater opportunities for researchers worldwide and make deep learning and Bayesian modeling more accessible.”
Pyro joins existing LF DL projects: Acumos AI, a platform and open source AI framework; Angel, a high-performance distributed machine learning platform based on Parameter Server; EDL, an Elastic Deep Learning framework designed to help cloud service providers to build cluster cloud services using deep learning frameworks; and Horovod, a distributed training framework for TensorFlow, Keras, and PyTorch.