# bytedance/byteps

A high performance and generic framework for distributed DNN training

Repository: https://github.com/bytedance/byteps
Canonical: https://ross.abutalabs.com/products/byteps
Language: Python
License: NOASSERTION
License Family: other
Topics: machine-learning, deep-learning, distributed-training, tensorflow, mxnet, keras, pytorch
Archived: true
Last push: 2023-10-03T18:02:27+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2626, "days_push": 1065, "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 3717, forks 491 (observed 2026-08-28T04:08:15.398771+00:00)

## What it is
BytePS is a high-performance parameter server framework for distributed deep neural network training, supporting TensorFlow, Keras, PyTorch, and MXNet over TCP or RDMA networks. It achieves significantly better scaling efficiency than Horovod+NCCL in many scenarios, such as ~90% scaling efficiency on BERT-large with 256 GPUs.

## Use cases
- train BERT-large across hundreds of GPUs with high scaling efficiency
- speed up distributed deep learning training beyond Horovod+NCCL
- run distributed training over RDMA networks
- add gradient compression to distributed training
- train TensorFlow, PyTorch, Keras, or MXNet models on a cluster

## When to choose
- you need maximum multi-GPU/multi-node training throughput on Ethernet or RDMA
- you train large models like BERT at scale and Horovod is a bottleneck
- you want a cross-framework parameter server for TensorFlow, PyTorch, or MXNet

## When to avoid
- you need a framework with frequent updates and broad community support
- your training is small-scale and single-node
- you require the latest framework features or newest PyTorch/TensorFlow versions

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, deep-learning, llm-training
- domain: machine-learning, deep-learning, gpu-computing, microservices
- platform: python, cloud
- tags: parameter-server, distributed-training, rdma, horovod-alternative, gradient-compression, linux, gpu, docker

## Member repositories
- bytedance/byteps (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.398771+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 2026-08-29T18:29:21.941509+00:00, confidence not recorded.
  - readme: https://github.com/bytedance/byteps (fetched 2026-08-28T04:08:15.398771+00:00, sha 73ed3e701083)
  - registry_pypi: https://pypi.org/pypi/byteps/json (fetched 2026-08-29T09:24:06.790816+00:00, sha a9e4d3066a96)
- Data as of 2026-08-30T08:39:29.467469+00:00.