# Weights & Biases

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Repository: https://github.com/wandb/wandb
Canonical: https://ross.abutalabs.com/products/weights-biases
Homepage: https://wandb.ai
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, experiment-track, deep-learning, keras, tensorflow, pytorch, hyperparameter-search, reinforcement-learning, mlops, data-science, collaboration, hyperparameter-optimization, reproducibility, hyperparameter-tuning, data-versioning, model-versioning, ml-platform, jax, ai
Last push: 2026-08-27T00:04:02+00:00
Link (homepage): https://wandb.ai

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 3449, "days_push": 7, "days_rel": 7, "gap_med": 14, "n_releases_24m": 48}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11239, forks 887 (observed 2026-08-28T04:10:46.527077+00:00)

## What it is
Weights & Biases (wandb) is a Python SDK and platform for tracking, visualizing, and managing machine learning experiments, including metrics logging, hyperparameter sweeps, and dataset/model versioning. It integrates with PyTorch, TensorFlow, Keras, and Jax and supports the full ML lifecycle from experimentation to production.

## Use cases
- track machine learning experiment metrics
- run hyperparameter sweeps and tuning
- version datasets and models
- compare training runs across experiments
- monitor LLM app evaluations
- log training curves from pytorch or tensorflow

## When to choose
- you need experiment tracking and visualization for ML training
- you want reproducible hyperparameter search across runs
- you need team collaboration and model versioning in an MLOps workflow

## When to avoid
- you need a fully self-hosted offline solution without a cloud account
- your project is not machine learning related

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, monitoring, data-visualization, benchmarking, sdk, cli
- domain: machine-learning, deep-learning, data-science, developer-tools
- platform: python, cross-platform, cloud
- tags: experiment-tracking, mlops, hyperparameter-tuning, model-registry, reproducibility, data-versioning, llm-observability

## Member repositories
- wandb/wandb (main) score 99
- wandb/examples (examples) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:46.527077+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 2026-08-29T17:16:25.412869+00:00, confidence not recorded.
  - readme: https://github.com/wandb/wandb (fetched 2026-08-28T04:10:46.527077+00:00, sha 1b1c224b4e6f)
  - homepage: https://wandb.ai (fetched 2026-08-29T08:14:59.102133+00:00, sha c3c626bc3f16)
- Data as of 2026-08-30T08:39:29.467469+00:00.