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
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Use Weights & Biases (W&B) when you need to:
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# 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
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()
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()
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}")
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
# 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})
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)
Automatically search for optimal hyperparameters.
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")
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
# 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} } }
Track datasets, models, and other files with lineage.
# 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)
# 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")
# 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')
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()
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)
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 )
# 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 )})
Create shareable reports in W&B UI:
wandb.init( project="my-project", tags=["baseline", "resnet50", "imagenet"], group="resnet-experiments", # Group related runs job_type="train" # Type of job )
# 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) })
# ✅ 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")
# 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})
import os # Enable offline mode os.environ["WANDB_MODE"] = "offline" wandb.init(project="my-project") # ... your code ... # Sync later # wandb sync <run_directory>
# Runs are automatically shareable via URL run = wandb.init(project="team-project") print(f"Share this URL: {run.url}")
references/sweeps.mdreferences/artifacts.mdreferences/integrations.mdMIT
mkdir -p ~/.hermes/skills/mlops/evaluation && curl -o ~/.hermes/skills/mlops/evaluation/SKILL.md https://raw.githubusercontent.com/NousResearch/hermes-agent/main/skills/mlops/evaluation/weights-and-biases/SKILL.md
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