# Weights & Biases — ML Experiment Tracking and Visualization Platform

> Track, compare, and visualize machine learning experiments with automatic logging for metrics, hyperparameters, and artifacts.

## Install

Save as a script file and run:

# Weights & Biases — ML Experiment Tracking and Visualization Platform

## Quick Use
```bash
pip install wandb
wandb login
```
```python
import wandb
wandb.init(project="my-project")
wandb.log({"loss": 0.5, "accuracy": 0.85})
```

## Introduction
Weights & Biases (W&B) is an open-source ML experiment tracking library that logs metrics, hyperparameters, model checkpoints, and datasets during training. The `wandb` Python client is open source and can report to either the hosted W&B service or a self-hosted server.

## What Weights & Biases Does
- Logs training metrics, system metrics, and hyperparameters automatically during model training
- Generates interactive dashboards for comparing runs across experiments
- Tracks datasets and model artifacts with versioning and lineage
- Supports hyperparameter sweeps with built-in Bayesian and grid search strategies
- Integrates natively with PyTorch, TensorFlow, Keras, Hugging Face Transformers, and Lightning

## Architecture Overview
The W&B client library instruments training loops by intercepting metric calls and streaming data to a backend. Locally, it writes run data to a `wandb/` directory. The data syncs to a W&B server (cloud or self-hosted) where it is stored in a structured format. The web UI queries this data to render charts, tables, and comparison views. Artifacts (models, datasets) are content-addressed and stored with dependency graphs for reproducibility.

## Self-Hosting & Configuration
- Install the client with `pip install wandb` on Python 3.7+
- Authenticate with `wandb login` using an API key from your W&B account
- Self-host the W&B server via Docker or Kubernetes using the official Helm chart
- Set `WANDB_BASE_URL` to point at a self-hosted instance instead of the cloud service
- Configure offline mode with `WANDB_MODE=offline` for air-gapped environments, then sync later

## Key Features
- Automatic logging hooks for major ML frameworks reduce instrumentation effort
- Interactive run comparison with parallel coordinates, scatter plots, and custom panels
- Artifact versioning tracks datasets and models with lineage and dependency graphs
- Sweep agent runs hyperparameter searches with configurable strategies
- Reports combine charts, markdown, and code into shareable documents

## Comparison with Similar Tools
- **MLflow** — Open-source experiment tracking with broader model registry; W&B provides richer visualization
- **TensorBoard** — Free visualization for TensorFlow; W&B adds cross-framework support and collaboration
- **Neptune** — Hosted experiment tracker; W&B's open-source client allows self-hosting
- **ClearML** — End-to-end MLOps; W&B focuses on experiment tracking and visualization
- **SwanLab** — Lightweight alternative with similar tracking features and a modern UI

## FAQ
**Q: Is Weights & Biases free to use?**
A: The client library is open source (Apache-2.0). The hosted service has a free tier for individuals and paid plans for teams. Self-hosting requires a license.

**Q: Can I use W&B without an internet connection?**
A: Yes. Set `WANDB_MODE=offline` to log locally, then sync with `wandb sync` when connectivity is restored.

**Q: Which ML frameworks does it support?**
A: PyTorch, TensorFlow, Keras, Hugging Face Transformers, Lightning, JAX, scikit-learn, XGBoost, and more via automatic integrations.

**Q: How does it compare to MLflow?**
A: Both track experiments and models. W&B offers more interactive visualization and collaboration features; MLflow provides a broader open-source model registry and deployment workflow.

## Sources
- https://github.com/wandb/wandb
- https://docs.wandb.ai/

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Source: https://tokrepo.com/en/workflows/asset-e311cdcf
Author: Script Depot