The LF AI Foundation, the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML) and deep learning (DL), is announcing today that hosted project Horovod is advancing from an Incubation level project to a Graduate level. This graduation is the result of Horovod demonstrating thriving adoption, an ongoing flow of contributions from multiple organizations, and both documented and structured open governance processes. Horovod has also achieved a Core Infrastructure Initiative Best Practices Badge, and demonstrated a strong commitment to community.
As an Incubation Project, Horovod utilized the LF AI Foundation’s various enablement services to foster its growth and adoption; including program management support, event coordination, legal services, and marketing services ranging from website creation to project promotion.
Horovod is a distributed training framework for TensorFlow, Keras and PyTorch, which improves speed, scale and resource allocation in machine learning training activities. It was open sourced by Uber, the project founder, and joined LF AI as an Incubation Project in December 2018.
“The journey of Horovod from Incubation to Graduation has been very impressive,” said Dr. Ibrahim Haddad, Executive Director of the LF AI Foundation. “The speed of development, the growth of its community, and its wide adoption is particularly noteworthy. Horovod has exceeded all of our graduation criteria and we’re proud to be its host Foundation and to support them across a number of services. As a Graduate project, our support to Horovod will continue to increase as needed. This graduation is our way to present Horovod as an advanced and mature open source technology ready for large scale deployments. Congratulations, Horovod!”
Uber uses Horovod for self-driving vehicles, fraud detection, and trip forecasting. It is also being used by Alibaba, Amazon and NVIDIA. Contributors to the project outside Uber include Amazon, IBM, Intel and NVIDIA.
“Since joining the LFAI, Horovod has developed into the industry-standard for training deep neural networks at scale, in every framework and on every platform,” said Travis Addair, Technical Lead for the Horovod project. “It’s a continued honor to collaborate with and learn from Horovod’s many exceptional contributors from across the deep learning community. This graduation is a major milestone for the Horovod project, and an acknowledgement of all the hard work and collaboration our contributors have put forward to make this project a success. As a graduated project, I am looking forward to broadening the reach of our community even further, working towards the goal of making deep learning training simple and intuitive to scale.”
Feature Roadmap for 2020
- Elastic Training / Fault Tolerance
- Horovod on Ray + Ray Tune Integration
- Ludwig + Horovod Spark Estimator integration
- TensorFlow / General Horovod Spark Estimator
- MXNet Horovod Spark Estimator (Amazon)
- Horovod Plugin Architecture (NVIDIA)
- Horovod Spark Dynamic GPU Allocation (NVIDIA)
Curious about how Horovod can make your model training faster and more scalable? Try out the framework now. And be sure to join the Horovod Announce and Horovod Technical-Discuss mailing lists to join the community and stay connected on the latest updates.
Congratulations to the Horovod 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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