weights-and-biases

# 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()

        # Log every 100 batches
        if batch_idx % 100 == 0:
            wandb.log({
                "loss": loss.item(),
                "epoch": epoch,
                "batch": batch_idx
            })

# Save model
torch.save(model.state_dict(), "model.pth")
wandb.save("model.pth")  # Upload to W&B

wandb.finish()
```

## Core Concepts

### 1. Projects and Runs

**Project**: Collection of related experiments
**Run**: Single execution of your training script

```python
# Create/use project
run = wandb.init(
    project="image-classification",
    name="resnet50-experiment-1",  # Optional run name
    tags=["baseline", "resnet"],    # Organize with tags
    notes="First baseline run"      # Add notes
)

# Each run has unique ID
print(f"Run ID: {run.id}")
print(f"Run URL: {run.url}")
```

### 2. Configuration Tracking

Track hyperparameters automatically:

```python
config = {
    # Model architecture
    "model": "ResNet50",
    "pretrained": True,

    # Training params
    "learning_rate": 0.001,
    "batch_size": 32,
    "epochs": 50,
    "optimizer": "Adam",

    # Data params
    "dataset": "ImageNet",
    "augmentation": "standard"
}

wandb.init(project="my-project", config=config)

# Access config during training
lr = wandb.config.learning_rate
batch_size = wandb.config.batch_size
```

### 3. Metric Logging

```python
# Log scalars
wandb.log({"loss": 0.5, "accuracy": 0.92})

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

# Log with custom x-axis
wandb.log({"loss": loss}, step=global_step)

# Log media (images, audio, video)
wandb.log({"examples": [wandb.Image(img) for img in images]})

# Log histograms
wandb.log({"gradients": wandb.Histogram(gradients)})

# Log tables
table = wandb.Table(columns=["id", "prediction", "ground_truth"])
wandb.log({"predictions": table})
```

### 4. Model Checkpointing

```python
import torch
import wandb

# Save model checkpoint
checkpoint = {
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'loss': loss,
}

torch.save(checkpoint, 'checkpoint.pth')

# Upload to W&B
wandb.save('checkpoint.pth')

# Or use Artifacts (recommended)
artifact = wandb.Artifact('model', type='model')
artifact.add_file('checkpoint.pth')
wandb.log_artifact(artifact)
```

## Hyperparameter Sweeps

Automatically search for optimal hyperparameters.

### Define Sweep Configuration

```python
sweep_config = {
    'method': 'bayes',  # or 'grid', 'random'
    'metric': {
        'name': 'val/accuracy',
        'goal': 'maximize'
    },
    'parameters': {
        'learning_rate': {
            'distribution': 'log_uniform',
            'min': 1e-5,
            'max': 1e-1
        },
        'batch_size': {
            'values': [16, 32, 64, 128]
        },
        'optimizer': {
            'values': ['adam', 'sgd', 'rmsprop']
        },
        'dropout': {
            'distribution': 'uniform',
            'min': 0.1,
            'max': 0.5
        }
    }
}

# Initialize sweep
sweep_id = wandb.sweep(sweep_config, project="my-project")
```

### Define Training Function

```python
def train():
    # Initialize run
    run = wandb.init()

    # Access sweep parameters
    lr = wandb.config.learning_rate
    batch_size = wandb.config.batch_size
    optimizer_name = wandb.config.optimizer

    # Build model with sweep config
    model = build_model(wandb.config)
    optimizer = get_optimizer(optimizer_name, lr)

    # Training loop
    for epoch in range(NUM_EPOCHS):
        train_loss = train_epoch(model, optimizer, batch_size)
        val_acc = validate(model)

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

# Run sweep
wandb.agent(sweep_id, function=train, count=50)  # Run 50 trials
```

### Sweep Strategies

```python
# Grid search - exhaustive
sweep_config = {
    'method': 'grid',