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Weights & Biases

Python library to track, visualize, and manage machine learning experiments and models.wandb/wandb

GitHub Download v0.31.0
Stars
11.3k
Forks
912
Licence
MIT
Language
Python
Last update
4 days ago
Type
Library

What it is

Weights & Biases (W&B) is a Python library and hosted platform for keeping track of machine learning work. You add a few lines to a training script to record hyperparameters and metrics like accuracy and loss, then view and compare those runs in a web dashboard. The README also describes covering the wider pipeline from datasets to production models, and offers cloud or self-hosted deployment.

  • Log training loss and accuracy from each run and compare runs in the dashboard
  • Record a configuration dictionary of hyperparameters with each experiment
  • Track multiple runs in a shared project for team review
  • Use the W&B Developer Guide integrations to connect with supported ML frameworks

Great for

  • Machine learning engineers who want to compare training runs over time
  • Researchers tuning hyperparameters across many experiments
  • Teams that need to share experiment results with colleagues
  • Developers using PyTorch, TensorFlow, or Keras who want tracking with minimal code changes

Not ideal for

  • People who only need to run a single script once with no need for records, since the setup adds overhead
  • Teams that cannot send data to a third-party service and do not want to self-host, because the default workflow uses the hosted W&B account
  • Users looking only for LLM app monitoring, since the README points to a separate product called Weave for that
Step-by-step guide: how to use Weights & BiasesInstall, first run, examples, tips and FAQs
Weights & Biases alternativesPyG vs Weights & BiasesONNX Runtime vs Weights & BiasesOptuna vs Weights & Biasesimgaug vs Weights & BiasesStable Baselines3 vs Weights & Biasespandas-profiling vs Weights & Biases

How to get it

  1. 1

    Install with a package manager

    You'll need: Python

    Install the wandb library

    pip install wandb

    Optionally configure your API key on this machine

    wandb login
  2. 2

    Run it

    • Sign up for a W&B account and create an API key at wandb.ai/settings
    • Install the library with pip install wandb
    • Add wandb.init() and run.log() calls to your training script
    • Run the script and open wandb.ai/home to view the recorded run
  3. 3

    Get the most out of it

    • Use the with wandb.init(...) as run: syntax so runs are marked finished or failed automatically
    • Pass hyperparameters through the config argument so they are saved with each run
    • Check the Developer Guide integrations page for framework-specific setup
    • Use a shared project name so teammates can compare runs together
    • Explore Weave from the README if you are building an LLM app

Downloads

Source code (.zip)main branch · straight from GitHubRelease v0.31.02 files incl. wandb-0.31.0.tar.gzOfficial website & docswandb.ai

Strengths

  • Simple API: a few lines like wandb.init() and run.log() add tracking to existing code
  • Broad framework integrations listed in the README
  • Open source under the MIT license with cloud and self-managed hosting options

Trade-offs

  • Full value depends on the hosted W&B account and dashboard, which adds a service dependency
  • The README is truncated, so details of offline use and data privacy controls are not covered here
  • Some hosting options (Dedicated Cloud, Self-Managed) are described only briefly in the README

How it compares

ProjectStarsTypeLevelUpdatedBest for
wandb (this)11.3kLibrarybeginner4 days agoMachine learning engineers who want to compare training runs over time
nni14.4kLibraryintermediate2.3 years agoResearchers comparing hyperparameter tuning algorithms such as TPE, Hyperband, or Bayesian methods
Made-With-ML49.9kLearning resourceintermediate8 months agoSoftware engineers who want to learn how ML models get deployed and maintained
onnxruntime22.2kLibraryintermediate4 days agoDevelopers who want to deploy a trained model inside an application or service
annotated_deep_learning_paper_implementations67.6kLearning resourceintermediate4 days agoStudents learning how transformers, GANs, or optimizers work internally
netron33.6kAppbeginner5 days agoEngineers who need to check a model's layers and tensor connections before deploying it

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.