# tensorflow/tfjs

A WebGL accelerated JavaScript library for training and deploying ML models.

Repository: https://github.com/tensorflow/tfjs
Canonical: https://ross.abutalabs.com/products/tfjs
Homepage: https://js.tensorflow.org
Language: TypeScript
License: Apache-2.0
License Family: permissive
Topics: deep-learning, typescript, webgl, javascript, machine-learning, neural-network, deep-neural-network, gpu-acceleration, wasm, web-assembly
Last push: 2026-06-23T00:04:38+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 88, release rhythm 28, longevity 100
- inputs: {"age_days": 3103, "days_push": 72, "days_rel": 681, "gap_med": 47, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 19134, forks 2030 (observed 2026-08-28T04:11:27.436165+00:00)

## What it is
TensorFlow.js is a hardware-accelerated JavaScript library for training and deploying machine learning models in the browser and Node.js. It provides low-level and Keras-like high-level APIs, multiple backends (CPU, WebGL, WASM, WebGPU, native Node), and converters for running pre-trained TensorFlow models in JavaScript.

## Use cases
- run machine learning models in the browser
- train neural networks in javascript
- convert a python tensorflow model to run on the web
- do transfer learning with webcam data in the browser
- run inference client-side without a server
- build an ml-powered web app with webgl acceleration
- use pretrained models like object detection in javascript

## When to choose
- you need ML inference or training directly in the browser with GPU acceleration
- you want to run or fine-tune TensorFlow models from JavaScript or Node.js
- you need client-side privacy by keeping data and models on the device
- you want a Keras-like layers API in TypeScript/JavaScript

## When to avoid
- you are training large-scale models where Python TensorFlow or PyTorch is more appropriate
- you need maximum native GPU performance for heavy training workloads
- your project has no JavaScript runtime and is purely server-side Python

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, gpu-computing, data-visualization
- domain: machine-learning, deep-learning, web-development, frontend
- platform: browser, cross-platform, wasm
- tags: tensorflow, webgl, webgpu, neural-networks, model-conversion, transfer-learning, pretrained-models, typescript, nodejs, web, gpu

## Member repositories
- tensorflow/tfjs (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:27.436165+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 2026-08-29T17:00:28.763589+00:00, confidence not recorded.
  - readme: https://github.com/tensorflow/tfjs (fetched 2026-08-28T04:11:27.436165+00:00, sha eb3594022d82)
  - homepage: https://js.tensorflow.org (fetched 2026-08-29T07:59:16.434399+00:00, sha ff013fdcc87d)
  - site_page: https://www.tensorflow.org/install (fetched 2026-08-29T07:59:16.444257+00:00, sha 584a762da891)
  - site_page: https://www.tensorflow.org/tfx/api_docs (fetched 2026-08-29T07:59:16.448363+00:00, sha 6977825696fe)
  - site_page: https://www.tensorflow.org/about (fetched 2026-08-29T07:59:16.452531+00:00, sha 817250744d91)
  - site_page: https://www.tensorflow.org/about/case-studies (fetched 2026-08-29T07:59:16.455092+00:00, sha e6701029eec0)
  - site_page: https://www.tensorflow.org/about/bib (fetched 2026-08-29T07:59:16.457509+00:00, sha 5ca8943386b9)
  - site_page: https://www.tensorflow.org/community/contribute (fetched 2026-08-29T07:59:16.450140+00:00, sha 77d394fa76e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.