weights-and-biaseslisted

# 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.i