# Weights And Biases

Track ML experiments with automatic logging, visualize training in real-time, optimize...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill weights-and-biases -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill weights-and-biases -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill weights-and-biases -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill weights-and-biases -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Weights & Biases: ML Experiment Tracking & MLOps

## When to Use This Skill

Use Weights & Biases (W&B) when you need to:
- **Track ML experiments** with automatic metric logging
- **Visualize training** in real-time dashboards
- **Compare runs** across hyperparameters and configurations
- **Optimize hyperparameters** with automated sweeps
- **Manage model registry** with versioning and lineage
- **Collaborate on ML projects** with team workspaces
- **Track artifacts** (datasets, models, code) with lineage

**Users**: 200,000+ ML practitioners | **GitHub Stars**: 10.5k+ | **Integrations**: 100+

## Installation

```bash
# Install W&B
pip install wandb

# Login (creates API key)
wandb login

# Or set API key programmatically
export WANDB_API_KEY=your_api_key_here
```

## Quick Start

### Basic Experiment Tracking

```python
import wandb

# Initialize a run
run = wandb.init(
    project="my-project",
    config={
        "learning_rate": 0.001,
        "epochs": 10,
        "batch_size": 32,
        "architecture": "ResNet50"
    }
)

# Training loop
for epoch in range(run.config.epochs):
    # Your training code
    train_loss = train_epoch()
    val_loss = validate()

    # Log metrics
    wandb.log({
        "epoch": epoch,
        "train/loss": train_loss,
        "val/loss": val_loss,
        "train/accuracy": train_acc,
        "val/accuracy": val_acc
    })

# Finish the run
wandb.finish()
```

### With PyTorch

```python
import torch
import wandb

# Initialize
wandb.init(project="pytorch-demo", config={
    "lr": 0.001,
    "epochs": 10
})

# Access config
config = wandb.config

# Training loop
for epoch in range(config.epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        # Forward pass
        output = model(data)
        loss = criterion(output, target)

        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/13-mlops/weights-and-biases/SKILL.md