horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
GraphCanon updated 3w · GitHub synced 3w · 26 views this month
Decision brief
Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.
Good fit when
- When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts.
- For environments where simplicity and quick setup of multi-GPU training are prioritized.
Avoid when
- Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility.
- Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Archived (4d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install horovodSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Horovod is a distributed deep learning training framework designed to make distributed deep learning fast and easy to use. It supports popular machine learning frameworks like TensorFlow, Keras, PyTorch, and Apache MXNet, facilitating multi-GPU and multi-node scaling.
Capability facts
- Languages
- python
Source: github.language · Aug 3, 2026
Categories
Tags
README
.. raw:: html
<p>This project is inactive. Feel free to fork.</p>
<p align="center"><img src="https://user-images.githubusercontent.com/16640218/34506318-84d0c06c-efe0-11e7-8831-0425772ed8f2.png" alt="Logo" width="200"/></p>
<br/>
Horovod (inactive)
.. raw:: html
.. image:: https://badge.fury.io/py/horovod.svg :target: https://badge.fury.io/py/horovod :alt: PyPI Version
.. image:: https://badge.buildkite.com/6f976bc161c69d9960fc00de01b69deb6199b25680a09e5e26.svg?branch=master :target: https://buildkite.com/horovod/horovod :alt: Build Status
.. image:: https://readthedocs.org/projects/horovod/badge/?version=latest :target: https://horovod.readthedocs.io/en/latest/ :alt: Documentation Status
.. image:: https://img.shields.io/badge/slack-chat-green.svg?logo=slack :target: https://forms.gle/cPGvty5hp31tGfg79 :alt: Slack
.. raw:: html
.. raw:: html
.. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg :target: https://img.shields.io/badge/License-Apache%202.0-blue.svg :alt: License
.. image:: https://app.fossa.com/api/projects/git%2Bgithub.com%2Fhorovod%2Fhorovod.svg?type=shield :target: https://app.fossa.com/projects/git%2Bgithub.com%2Fhorovod%2Fhorovod?ref=badge_shield :alt: FOSSA Status
.. image:: https://bestpractices.coreinfrastructure.org/projects/2373/badge :target: https://bestpractices.coreinfrastructure.org/projects/2373 :alt: CII Best Practices
.. image:: https://pepy.tech/badge/horovod :target: https://pepy.tech/project/horovod :alt: Downloads
.. raw:: html
.. inclusion-marker-start-do-not-remove
|
Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use.
.. raw:: html
Horovod is hosted by the LF AI & Data Foundation <https://lfdl.io>_ (LF AI & Data). If you are a company that is deeply
committed to using open source technologies in artificial intelligence, machine, and deep learning, and want to support
the communities of open source projects in these domains, consider joining the LF AI & Data Foundation. For details
about who's involved and how Horovod plays a role, read the Linux Foundation announcement <https://lfdl.io/press/2018/12/13/lf-deep-learning-welcomes-horovod-distributed-training-framework-as-newest-project/>_.
|
.. contents::
|
Documentation
Latest Release <https://horovod.readthedocs.io/en/stable>_master <https://horovod.readthedocs.io/en/latest>_
|
Why Horovod?
The primary motivation for this project is to make it easy to take a single-GPU training script and successfully scale it to train across many GPUs in parallel. This has two aspects:
- How much modification does one have to make to a program to make it distributed, and how easy is it to run it?
- How much faster would it run in distributed mode?
Internally at Uber we found the MPI model to be much more straightforward and require far less code changes than previous
solutions such as Distributed TensorFlow with parameter servers. Once a training script has been written for scale with
Horovod, it can run on a single-GPU, multiple-GPUs, or even multiple hosts without any further code changes.
See the Usage <#usage>__ section for more details.
In addition to being easy to use, Horovod is fast. Below is a chart representing the benchmark that was done on 128 servers with 4 Pascal GPUs each connected by RoCE-capable 25 Gbit/s network:
.. image:: https://user-images.githubusercontent.com/16640218/38965607-bf5c46ca-4332-11e8-895a-b9c137e86013.png :alt: 512-GPU Benchmark
Horovod achie
For agents
This page has a .md twin and JSON over the API.