# horovod/horovod

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

Repository: https://github.com/horovod/horovod
Canonical: https://ross.abutalabs.com/products/horovod
Homepage: http://horovod.ai
Language: Python
License: NOASSERTION
License Family: other
Topics: tensorflow, uber, machine-learning, machinelearning, mpi, baidu, deep-learning, deeplearning, keras, pytorch, mxnet, spark, ray
Archived: true
Last push: 2026-07-29T17:27:00+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 8, longevity 100
- inputs: {"age_days": 3311, "days_push": 35, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14688, forks 2233 (observed 2026-08-28T04:11:07.451046+00:00)

## What it is
Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet, originally developed at Uber. It lets users scale single-GPU training scripts to run across many GPUs with minimal code changes, using MPI-style allreduce and integration with Spark and Ray. The project is now inactive.

## Use cases
- scale a single-GPU PyTorch training script to multiple GPUs
- run distributed TensorFlow training across a cluster
- train deep learning models on Spark clusters
- use NCCL-based allreduce for fast multi-node GPU training
- unify data processing and model training in one pipeline

## When to choose
- you need multi-GPU or multi-node data-parallel training with PyTorch, TensorFlow, Keras, or MXNet
- you want minimal code changes to scale an existing training script
- you are maintaining an existing Horovod-based training pipeline

## When to avoid
- starting a new project, since Horovod is officially inactive and unmaintained
- you only need single-GPU training
- you prefer framework-native distributed training like torch.distributed or tf.distribute

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, llm-training
- domain: machine-learning, deep-learning, microservices, gpu-computing
- platform: python, cloud
- tags: distributed-training, mpi, nccl, tensorflow, pytorch, keras, mxnet, spark, data-parallelism, linux, gpu, docker

## Member repositories
- horovod/horovod (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:07.451046+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:07:19.803006+00:00, confidence not recorded.
  - readme: https://github.com/horovod/horovod (fetched 2026-08-28T04:11:07.451046+00:00, sha ed4b581fad1e)
  - homepage: http://horovod.ai (fetched 2026-08-29T08:05:33.806493+00:00, sha 019c64bd1fe7)
  - site_page: https://horovod.ai/getting-started (fetched 2026-08-29T08:05:33.815388+00:00, sha ea382b085226)
  - registry_pypi: https://pypi.org/pypi/horovod/json (fetched 2026-08-29T08:05:33.817237+00:00, sha b4c69987a796)
- Data as of 2026-08-30T08:39:29.467469+00:00.