Horovod (machine learning)

Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet. Horovod is hosted under the Linux Foundation AI (LF AI).[3] Horovod has the goal of improving the speed, scale, and resource allocation when training a machine learning model[4]

Horovod
Developer(s)Uber
Initial releaseAugust 9, 2017 (2017-08-09)[1]
Stable release
v0.19.5[2] / May 6, 2020 (2020-05-06)
Repositorygithub.com/horovod/horovod/
Written inPython, C++, CUDA
PlatformLinux, macOS, Windows
TypeArtificial intelligence ecosystem
LicenseApache License 2.0
Websiteeng.uber.com/horovod/

Features
gollark: I don't think you can detect that.
gollark: That's where PotatOS puts the PX mode hooks and also heavlisp.
gollark: I would patch `load` probably.
gollark: It's in a secure facility on SwitchCraft, GTech Site Null.
gollark: You could use the PotatOS root key as the root.

See also

References
  1. Alex Sergeev (August 9, 2017). "Release v0.9.0 · horovod/horovod". horovod. Retrieved July 9, 2020. Initial release
  2. "Releases · horovod/horovod". horovod. Retrieved July 9, 2020.
  3. Khari Johnson (December 13, 2018). "Uber brings Horovod project for distributed deep learning to Linux Foundation". VentureBeat. Retrieved July 9, 2020.
  4. "Projects - LF AI". Linux Foundation - LF AI. Retrieved July 9, 2020. Horovod, a distributed training framework for TensorFlow, Keras and PyTorch, improves speed, scale and resource allocation in machine learning training activities. Uber uses Horovod for self-driving vehicles, fraud detection, and trip forecasting. It is also being used by Alibaba, Amazon and NVIDIA.
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