# tensorflow/mesh

Mesh TensorFlow: Model Parallelism Made Easier

Repository: https://github.com/tensorflow/mesh
Canonical: https://ross.abutalabs.com/products/tensorflow-mesh
Language: Python
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2023-11-17T19:39:54+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": 2904, "days_push": 1020, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1630, forks 255 (observed 2026-08-28T04:05:13.969700+00:00)

## What it is
Mesh TensorFlow is a Python library and embedded language for specifying distributed tensor computations, letting users define how model dimensions are split across a mesh of processors. It is implemented as a layer over TensorFlow and supports model parallelism, data parallelism, and mixed strategies for training and inference.

## Use cases
- train a model whose parameters don't fit on a single device
- split a large language model across multiple GPUs or TPUs
- run model parallelism with data parallelism combined
- perform low-latency parallel inference at batch size 1
- train models with activations too large for one device
- specify custom tensor sharding layouts across a processor mesh

## When to choose
- you need model parallelism for very large models in TensorFlow
- you want declarative control over how tensors are split across devices
- you are training huge language models on TPU/GPU meshes

## When to avoid
- you only need simple data-parallel training
- you use PyTorch or JAX instead of TensorFlow
- you want actively developed tooling - the project is in maintenance mode

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, llm-training, gpu-computing
- domain: machine-learning, deep-learning, large-language-models, microservices
- platform: python
- tags: model-parallelism, distributed-training, tensorflow, tensor-sharding, data-parallelism, gpu, linux

## Member repositories
- tensorflow/mesh (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:13.969700+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 2026-08-30T03:48:03.097310+00:00, confidence not recorded.
  - readme: https://github.com/tensorflow/mesh (fetched 2026-08-28T04:05:13.969700+00:00, sha 1f0eab5e283a)
- Data as of 2026-08-30T08:39:29.467469+00:00.