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name: weights-and-biases description: "W&B: log ML experiments, sweeps, model registry, dashboards." version: 1.0.1 author: Orchestra Research license: MIT dependencies: [wandb] platforms: [linux, macos, windows] metadata: hermes: tags: [MLOps, Weights And Biases, WandB, Experiment Tracking, Hyperparameter Tuning, Model Registry, Collaboration, Real-Time Visualization, PyTorch, TensorFlow, HuggingFace]

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_values',
            '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',
    'parameters': {
        'lr': {'values': [0.001, 0.01, 0.1]},
        'batch_size': {'values': [16, 32, 64]}
    }
}
# Random search
sweep_config = {
    'method': 'random',
    'parameters': {
        'lr': {'distribution': 'uniform', 'min': 0.0001, 'max': 0.1},
        'dropout': {'distribution': 'uniform', 'min': 0.1, 'max': 0.5}
    }
}
# Bayesian optimization (recommended)
sweep_config = {
    'method': 'bayes',
    'metric': {'name': 'val/loss', 'goal': 'minimize'},
    'parameters': {
        'lr': {'distribution': 'log_uniform_values', 'min': 1e-5, 'max': 1e-1}
    }
}

Artifacts

Track datasets, models, and other files with lineage.

Log Artifacts

python
# Create artifact
artifact = wandb.Artifact(
    name='training-dataset',
    type='dataset',
    description='ImageNet training split',
    metadata={'size': '1.2M images', 'split': 'train'}
)
# Add files
artifact.add_file('data/train.csv')
artifact.add_dir('data/images/')
# Log artifact
wandb.log_artifact(artifact)

Use Artifacts

python
# Download and use artifact
run = wandb.init(project="my-project")
# Download artifact
artifact = run.use_artifact('training-dataset:latest')
artifact_dir = artifact.download()
# Use the data
data = load_data(f"{artifact_dir}/train.csv")

Model Registry

python
# Log model as artifact
model_artifact = wandb.Artifact(
    name='resnet50-model',
    type='model',
    metadata={'architecture': 'ResNet50', 'accuracy': 0.95}
)
model_artifact.add_file('model.pth')
wandb.log_artifact(model_artifact, aliases=['best', 'production'])
# Link to model registry
run.link_artifact(model_artifact, 'model-registry/production-models')

Integration Examples

HuggingFace Transformers

python
from transformers import Trainer, TrainingArguments
import wandb
# Initialize W&B
wandb.init(project="hf-transformers")
# Training arguments with W&B
training_args = TrainingArguments(
    output_dir="./results",
    report_to="wandb",  # Enable W&B logging
    run_name="bert-finetuning",
    logging_steps=100,
    save_steps=500
)
# Trainer automatically logs to W&B
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)
trainer.train()

PyTorch Lightning

python
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import WandbLogger
import wandb
# Create W&B logger
wandb_logger = WandbLogger(
    project="lightning-demo",
    log_model=True  # Log model checkpoints
)
# Use with Trainer
trainer = Trainer(
    logger=wandb_logger,
    max_epochs=10
)
trainer.fit(model, datamodule=dm)

Keras/TensorFlow

python
import wandb
from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint
# Initialize
wandb.init(project="keras-demo")
# Add callbacks (the monolithic WandbCallback was removed;
# use the dedicated callbacks from wandb.integration.keras instead)
model.fit(
    x_train, y_train,
    validation_data=(x_val, y_val),
    epochs=10,
    callbacks=[
        WandbMetricsLogger(),                        # Auto-logs metrics
        WandbModelCheckpoint("models/model-{epoch}")  # Saves checkpoints
    ]
)

Visualization & Analysis

Custom Charts

python
# Log custom visualizations
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(x, y)
wandb.log({"custom_plot": wandb.Image(fig)})
# Log confusion matrix
wandb.log({"conf_mat": wandb.plot.confusion_matrix(
    probs=None,
    y_true=ground_truth,
    preds=predictions,
    class_names=class_names
)})

Reports

Create shareable reports in W&B UI:

  • Combine runs, charts, and text
  • Markdown support
  • Embeddable visualizations
  • Team collaboration

Best Practices

1. Organize with Tags and Groups

python
wandb.init(
    project="my-project",
    tags=["baseline", "resnet50", "imagenet"],
    group="resnet-experiments",  # Group related runs
    job_type="train"             # Type of job
)

2. Log Everything Relevant

python
# Log system metrics
wandb.log({
    "gpu/util": gpu_utilization,
    "gpu/memory": gpu_memory_used,
    "cpu/util": cpu_utilization
})
# Log code version
wandb.log({"git_commit": git_commit_hash})
# Log data splits
wandb.log({
    "data/train_size": len(train_dataset),
    "data/val_size": len(val_dataset)
})

3. Use Descriptive Names

python
# ✅ Good: Descriptive run names
wandb.init(
    project="nlp-classification",
    name="bert-base-lr0.001-bs32-epoch10"
)
# ❌ Bad: Generic names
wandb.init(project="nlp", name="run1")

4. Save Important Artifacts

python
# Save final model
artifact = wandb.Artifact('final-model', type='model')
artifact.add_file('model.pth')
wandb.log_artifact(artifact)
# Save predictions for analysis
predictions_table = wandb.Table(
    columns=["id", "input", "prediction", "ground_truth"],
    data=predictions_data
)
wandb.log({"predictions": predictions_table})

5. Use Offline Mode for Unstable Connections

python
import os
# Enable offline mode
os.environ["WANDB_MODE"] = "offline"
wandb.init(project="my-project")
# ... your code ...
# Sync later
# wandb sync <run_directory>

Team Collaboration

Share Runs

python
# Runs are automatically shareable via URL
run = wandb.init(project="team-project")
print(f"Share this URL: {run.url}")

Team Projects

  • Create team account at wandb.ai
  • Add team members
  • Set project visibility (private/public)
  • Use team-level artifacts and model registry

Pricing

  • Free: Unlimited public projects, 100GB storage
  • Academic: Free for students/researchers
  • Teams: $50/seat/month, private projects, unlimited storage
  • Enterprise: Custom pricing, on-prem options

Resources

  • Documentation: https://docs.wandb.ai
  • GitHub: https://github.com/wandb/wandb (10.5k+ stars)
  • Examples: https://github.com/wandb/examples
  • Community: https://wandb.ai/community
  • Discord: https://wandb.me/discord

See Also

  • references/sweeps.md - Comprehensive hyperparameter optimization guide
  • references/artifacts.md - Data and model versioning patterns
  • references/integrations.md - Framework-specific examples
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