Uber has contributed the Horovod machine learning project to the LF Deep Learning Foundation. Horovod, a distributed training framework for TensorFlow, Keras and PyTorch, is said to improve speed, scale and resource allocation in machine learning training activities.

Commenting on the Horovod project, Ibrahim Haddad, Linux Foundation Director of Research, said: “This project has proven highly effective in training machine learning models quickly and efficiently, and we look forward to working to further grow the Horovod community and encourage adoption of this exciting project.”

Horovod makes it easy to take a single-GPU TensorFlow program and successfully train it on many GPUs faster, the official release said.

The project is also said to achieve improved GPU resource usage figures. It uses advanced algorithms and leverages features of high-performance networks to provide data scientists, researchers and AI developers with tooling to scale their deep learning models with ease and high performance.

Real-world activities Uber has used Horovod to support include 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.

Horovod joins existing LF Deep Learning projects: Acumos AI, Angel, and EDL.