artifacts

# Artifacts & Model Registry Guide Complete guide to data versioning and model management with W&B Artifacts. ## Table of Contents - What are Artifacts - Creating Artifacts - Using Artifacts - Model Registry - Versioning & Lineage - Best Practices ## What are Artifacts Artifacts are versioned datasets, models, or files tracked with lineage. **Key Features:** - Automatic versioning (v0, v1, v2...) - Lineage tracking (which runs produced/used artifacts) - Efficient storage (deduplication) - Collaboration (team-wide access) - Aliases (latest, best, production) **Common Use Cases:** - Dataset versioning - Model checkpoints - Preprocessed data - Evaluation results - Configuration files ## Creating Artifacts ### Basic Dataset Artifact python import wandb run = wandb.init(project="my-project") # Create artifact dataset = wandb.Artifact( name='training-data', type='dataset', description='ImageNet training split with augmentations', metadata={ 'size': '1.2M images', 'format': 'JPEG', 'resolution': '224x224' } ) # Add files dataset.add_file('data/train.csv') # Single file dataset.add_dir('data/images') # Entire directory dataset.add_reference('s3://bucket/data') # Cloud reference # Log artifact run.log_artifact(dataset) wandb.finish() ### Model Artifact python import torch import wandb run = wandb.init(project="my-project") # Train model model = train_model() # Save model torch.save(model.state_dict(), 'model.pth') # Create model artifact model_artifact = wandb.Artifact( name='resnet50-classifier', type='model', description='ResNet50 trained on ImageNet', metadata={ 'architecture': 'ResNet50', 'accuracy': 0.95, 'loss': 0.15, 'epochs': 50, 'framework': 'PyTorch' } ) # Add model file model_artifact.add_file('model.pth') # Add config model_artifact.add_file('config.yaml') # Log with aliases run.log_artifact(model_artifact, aliases=['latest', 'best']) wandb.finish() ### Preprocessed Data Artifact python import pandas as pd import wandb run = wandb.init(project="nlp-project") # Preprocess data df = pd.read_csv('raw_data.csv') df_processed = preprocess(df) df_processed.to_csv('processed_data.csv', index=False) # Create artifact processed_data = wandb.Artifact( name='processed-text-data', type='dataset', metadata={ 'rows': len(df_processed), 'columns': list(df_processed.columns),