Weights & Biases Integration

The Weights & Biases integration provides cloud-based experiment tracking with rich visualization, collaboration features, and hyperparameter optimization.

Installation

pip install "zenml[wandb]"

This installs:

wandb>=0.12.12,<1.0.0 - W&B SDK
weave>=0.51.33,<1.0.0 - W&B Weave for ML observability
Pillow>=9.1.0 - Image processing for visualizations

Available Components

W&B Experiment Tracker

Track experiments, metrics, and artifacts with Weights & Biases

W&B Experiment Tracker

Track experiments and log metrics, parameters, and artifacts to Weights & Biases.

Configuration

With API Key:

zenml experiment-tracker register wandb-tracker \
    --flavor=wandb \
    --entity=my-wandb-team \
    --project_name=zenml-experiments \
    --api_key=your-wandb-api-key

Using Environment Variable:

# Set API key in environment
export WANDB_API_KEY=your-wandb-api-key

# Register without explicit key
zenml experiment-tracker register wandb-tracker \
    --flavor=wandb \
    --entity=my-wandb-team \
    --project_name=zenml-experiments

Using W&B CLI Login:

# Login via CLI
wandb login

# Register tracker
zenml experiment-tracker register wandb-tracker \
    --flavor=wandb \
    --entity=my-wandb-team \
    --project_name=zenml-experiments

Configuration Parameters:

entity - W&B team/username (optional, defaults to default entity)
project_name - W&B project name (optional, defaults to “zenml-runs”)
api_key - W&B API key (optional if set in environment)

Getting Your API Key

Go to W&B Settings
Find “API keys” section
Copy your API key
Use it in configuration or set as WANDB_API_KEY

Usage in Steps

Basic Logging:

from zenml import step, pipeline
import wandb

@step(experiment_tracker="wandb-tracker")
def train_model(data: pd.DataFrame) -> Model:
    # Initialize run (automatically done by ZenML)
    config = {
        "learning_rate": 0.001,
        "epochs": 10,
        "batch_size": 32,
    }
    wandb.config.update(config)
    
    # Training loop
    for epoch in range(10):
        train_loss = train_epoch(model, data)
        val_loss = validate(model, val_data)
        
        # Log metrics
        wandb.log({
            "epoch": epoch,
            "train_loss": train_loss,
            "val_loss": val_loss,
        })
    
    return model

Using ZenML Experiment Tracker Interface:

from zenml import step
from zenml.client import Client

experiment_tracker = Client().active_stack.experiment_tracker

@step(experiment_tracker="wandb-tracker")
def train_model() -> Model:
    # Log parameters
    experiment_tracker.log_params({
        "learning_rate": 0.001,
        "n_estimators": 100,
    })
    
    # Log metrics
    for epoch in range(100):
        loss = train_epoch()
        experiment_tracker.log_metrics(
            {"loss": loss, "epoch": epoch},
            step=epoch
        )
    
    return model

Advanced Logging

Log Images:

import wandb
import matplotlib.pyplot as plt

@step(experiment_tracker="wandb-tracker")
def visualize_results(predictions: np.ndarray) -> None:
    # Log matplotlib figure
    fig, ax = plt.subplots()
    ax.plot(predictions)
    wandb.log({"predictions_plot": wandb.Image(fig)})
    plt.close()
    
    # Log PIL image
    from PIL import Image
    img = Image.open("output.png")
    wandb.log({"output_image": wandb.Image(img)})
    
    # Log image from array
    wandb.log({"heatmap": wandb.Image(array_data)})

Log Tables:

import wandb

@step(experiment_tracker="wandb-tracker")
def log_evaluation_results(predictions: pd.DataFrame) -> None:
    # Create W&B table
    table = wandb.Table(dataframe=predictions)
    wandb.log({"predictions_table": table})
    
    # Or create manually
    table = wandb.Table(
        columns=["input", "prediction", "label"],
        data=[[1.0, 0.9, 1], [2.0, 0.1, 0]]
    )
    wandb.log({"results": table})

Log Artifacts:

import wandb

@step(experiment_tracker="wandb-tracker")
def save_model(model: Model) -> None:
    # Save model locally first
    model.save("model.pkl")
    
    # Log as artifact
    artifact = wandb.Artifact("model", type="model")
    artifact.add_file("model.pkl")
    wandb.log_artifact(artifact)
    
    # Or log directory
    artifact = wandb.Artifact("training-data", type="dataset")
    artifact.add_dir("data/")
    wandb.log_artifact(artifact)

Log Histograms:

import wandb
import numpy as np

@step(experiment_tracker="wandb-tracker")
def log_distributions(data: np.ndarray) -> None:
    wandb.log({"distribution": wandb.Histogram(data)})

Log Confusion Matrix:

import wandb
from sklearn.metrics import confusion_matrix

@step(experiment_tracker="wandb-tracker")
def evaluate_model(y_true: np.ndarray, y_pred: np.ndarray) -> None:
    cm = confusion_matrix(y_true, y_pred)
    wandb.log({
        "confusion_matrix": wandb.plot.confusion_matrix(
            probs=None,
            y_true=y_true,
            preds=y_pred,
            class_names=["Class 0", "Class 1"],
        )
    })

Run Configuration

Custom Run Names and Tags:

import wandb

@step(experiment_tracker="wandb-tracker")
def train_model() -> Model:
    # Set run name and tags
    wandb.run.name = "experiment-v2-lr-0.001"
    wandb.run.tags = ["baseline", "v2", "production"]
    wandb.run.notes = "Testing new architecture with reduced learning rate"
    
    # Training code
    ...

Group Runs:

import wandb

@step(experiment_tracker="wandb-tracker")
def hyperparameter_search() -> None:
    # Group related runs
    for lr in [0.001, 0.01, 0.1]:
        with wandb.init(
            project="zenml-experiments",
            group="lr-search",
            job_type="train",
        ):
            wandb.config.update({"learning_rate": lr})
            train_and_log(lr)

Framework Integration

PyTorch:

import wandb
import torch

@step(experiment_tracker="wandb-tracker")
def train_pytorch_model() -> None:
    # Watch model gradients and parameters
    model = MyModel()
    wandb.watch(model, log="all", log_freq=100)
    
    for epoch in range(epochs):
        loss = train_epoch(model)
        wandb.log({"loss": loss})

TensorFlow/Keras:

import wandb
from wandb.keras import WandbCallback

@step(experiment_tracker="wandb-tracker")
def train_keras_model() -> None:
    model = build_model()
    
    # Use W&B callback
    model.fit(
        X_train, y_train,
        validation_data=(X_val, y_val),
        callbacks=[WandbCallback()],
    )

Scikit-learn:

import wandb
from sklearn.ensemble import RandomForestClassifier

@step(experiment_tracker="wandb-tracker")
def train_sklearn_model() -> None:
    model = RandomForestClassifier(
        n_estimators=100,
        max_depth=5,
    )
    
    # Log hyperparameters
    wandb.config.update(model.get_params())
    
    model.fit(X_train, y_train)
    
    # Log metrics
    train_score = model.score(X_train, y_train)
    val_score = model.score(X_val, y_val)
    
    wandb.log({
        "train_accuracy": train_score,
        "val_accuracy": val_score,
    })

Complete Stack Example

# Register experiment tracker
zenml experiment-tracker register wandb-prod \
    --flavor=wandb \
    --entity=my-ml-team \
    --project_name=production-models \
    --api_key=your-wandb-api-key

# Create stack
zenml stack register wandb-stack \
    -o local \
    -a local \
    -e wandb-prod

# Activate
zenml stack set wandb-stack

W&B Features

Sweeps (Hyperparameter Optimization)

import wandb
from zenml import step

@step(experiment_tracker="wandb-tracker")
def hyperparameter_sweep() -> None:
    # Define sweep configuration
    sweep_config = {
        "method": "bayes",
        "metric": {"name": "val_accuracy", "goal": "maximize"},
        "parameters": {
            "learning_rate": {"min": 0.0001, "max": 0.1},
            "batch_size": {"values": [16, 32, 64]},
            "epochs": {"value": 10},
        },
    }
    
    sweep_id = wandb.sweep(sweep_config, project="zenml-experiments")
    wandb.agent(sweep_id, function=train_function, count=20)

Reports

Create shareable reports in W&B UI:

Go to your project page
Click “Create Report”
Add charts, tables, and markdown
Share with team or make public

Workspaces

Organize experiments in workspaces:

Filter runs by tags, parameters, or metrics
Create custom charts and visualizations
Compare multiple runs side-by-side

Best Practices

Use Tags for Organization

Tag runs for easy filtering:

wandb.run.tags = [
    "baseline",
    "v2",
    "production",
    "high-priority",
]

Log System Metrics

Enable system monitoring:

wandb.init(monitor_gym=True)  # GPU, CPU, memory

Use Artifacts for Reproducibility

Version datasets and models:

# Log dataset
dataset_artifact = wandb.Artifact("training-data", type="dataset")
dataset_artifact.add_file("data.csv")
wandb.log_artifact(dataset_artifact)

# Use in another run
artifact = wandb.use_artifact("training-data:latest")
data_path = artifact.file()

Group Related Runs

W&B vs MLflow

Feature	W&B	MLflow
Hosting	Cloud-based	Self-hosted or cloud
UI	Rich, modern	Functional
Collaboration	Built-in	Limited
Hyperparameter search	Built-in sweeps	External tools
Artifacts	Native support	Basic support
Cost	Free tier + paid	Free (self-hosted)
Setup complexity	Minimal	Moderate
Offline mode	Limited	Full support

Common Issues

Offline Mode

To work without internet:

export WANDB_MODE=offline
# Run pipeline
# Sync later:
wandb sync wandb/offline-run-*

Rate Limiting

If you hit rate limits:

Reduce logging frequency
Batch log calls together
Contact W&B for higher limits

Large Artifacts

For large files:

Use artifact references instead of uploads
Compress data before logging
Use external storage with references

Next Steps

MLflow Integration

Compare with MLflow tracking

Experiment Tracking

Learn more about experiment tracking

Vertex AI Integration

Combine with GCP Vertex Experiments

W&B Docs

Official Weights & Biases documentation

Getting Started

Core Concepts

Guides

Stack Components

Integrations

Advanced

Deployment

Weights & Biases Integration

Installation

Available Components

W&B Experiment Tracker

W&B Experiment Tracker

Configuration

Getting Your API Key

Usage in Steps

Advanced Logging

Run Configuration

Framework Integration

Complete Stack Example

W&B Features

Sweeps (Hyperparameter Optimization)

Reports

Workspaces

Best Practices

W&B vs MLflow

Common Issues

Next Steps

MLflow Integration

Experiment Tracking

Vertex AI Integration

W&B Docs

Build docs developers (and LLMs) love

Getting Started

Core Concepts

Guides

Stack Components

Integrations

Advanced

Deployment

​Installation

​Available Components

W&B Experiment Tracker

​W&B Experiment Tracker

​Configuration

​Getting Your API Key

​Usage in Steps

​Advanced Logging

​Run Configuration

​Framework Integration

​Complete Stack Example

​W&B Features

​Sweeps (Hyperparameter Optimization)

​Reports

​Workspaces

​Best Practices

​W&B vs MLflow

​Common Issues

​Next Steps

MLflow Integration

Experiment Tracking

Vertex AI Integration

W&B Docs

Build docs developers (and LLMs) love

Installation

Available Components

W&B Experiment Tracker

Configuration

Getting Your API Key

Usage in Steps

Advanced Logging

Run Configuration

Framework Integration

Complete Stack Example

W&B Features

Sweeps (Hyperparameter Optimization)

Reports

Workspaces

Best Practices

W&B vs MLflow

Common Issues

Next Steps