wandb

wandb/wandb

Weights & Biases platform for model training and management

GitHub Website

GraphCanon updated 2w · GitHub synced 2w

11k stars880 forksLast push 2w Python MIT

Decision brief

wandb excels in streamlined experiment tracking and model versioning across multiple machine learning frameworks.

Good fit when

Need extensive collaboration features for teams working on deep-learning projects
Requiring a platform that supports all major ML frameworks like Tensorflow, PyTorch, Jax

Avoid when

Looking for a lightweight solution without extensive collaboration features
Focusing on simple models where detailed experiment tracking is unnecessary

Observed Jul 17, 2026 · Source: enrich:decision_facts

wandb alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Very active (0d since push); As of 2w
Provenance: Not a fork · Organization account; As of 2w
Security (OSV): No lockfile; As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install wandb

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Provides tools to train and fine-tune models, manage experimentation, hyperparameter tuning, and transition from experiment to production.

Capability facts

CLI: CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 3, 2026
Languages: python
Source: github.language+pyproject.toml · Aug 3, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 3, 2026)

pip install wandb

Source link

README

Install the wandb library

pip install wandb

W&B Hosting Options

Weights & Biases is available in the cloud or installed on your private infrastructure. Set up a W&B Server in a production environment in one of three ways:

Multi-tenant Cloud: Fully managed platform deployed in W&B’s Google Cloud Platform (GCP) account in GCP’s North America regions.
Dedicated Cloud: Single-tenant, fully managed platform deployed in W&B’s AWS, GCP, or Azure cloud accounts. Each Dedicated Cloud instance has its own isolated network, compute and storage from other W&B Dedicated Cloud instances.
Self-Managed: Deploy W&B Server on your AWS, GCP, or Azure cloud account or within your on-premises infrastructure.

See the Hosting documentation in the W&B Developer Guide for more information.

License

MIT License

For agents

This page has a .md twin and JSON over the API.

/tools/wandb-wandb.md GET /api/graphcanon/tools/wandb-wandb GET /api/graphcanon/graph?tool=wandb-wandb