Weights And Biases — W&B: log ML experiments, sweeps, model registry, dashboards

Weights And Biases

W&B: log ML experiments, sweeps, model registry, dashboards.

Skill metadata


Source	Bundled (installed by default)
Path	`skills/mlops/evaluation/weights-and-biases`
Version	`1.0.0`
Author	Orchestra Research
License	MIT
Dependencies	`wandb`
Tags	`MLOps`, `Weights And Biases`, `WandB`, `Experiment Tracking`, `Hyperparameter Tuning`, `Model Registry`, `Collaboration`, `Real-Time Visualization`, `PyTorch`, `TensorFlow`, `HuggingFace`

Reference: full SKILL.md

Info The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

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

# 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

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

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

# 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:

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

# 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

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

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

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

# 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', 'min': 1e-5, 'max': 1e-1}
    }
}

Artifacts

Track datasets, models, and other files with lineage.

Log Artifacts

# 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

# 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

# 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

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

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

import wandb
from wandb.keras import WandbCallback

# Initialize
wandb.init(project="keras-demo")

# Add callback
model.fit(
    x_train, y_train,
    validation_data=(x_val, y_val),
    epochs=10,
    callbacks=[WandbCallback()]  # Auto-logs metrics
)

Visualization & Analysis

Custom Charts

# 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

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

# 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

# ✅ 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

# 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

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

# 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