Overview
Weights & Biases (wandb) tracks ML experiments with rich visualizations, hyperparameter sweeps, dataset versioning, model registry, and collaborative dashboards. Industry standard for experiment tracking across ML teams.
Installation
uv pip install wandb
wandb login # authenticate with API key
Experiment Tracking
import wandb
wandb.init(project="my_project", config={
"learning_rate": 0.001,
"batch_size": 32,
"architecture": "transformer",
})
for epoch in range(10):
loss = train_one_epoch()
wandb.log({"train_loss": loss, "val_loss": val_loss, "epoch": epoch})
wandb.finish()
Hyperparameter Sweep
sweep_config = {
"method": "bayes",
"metric": {"name": "val_loss", "goal": "minimize"},
"parameters": {"lr": {"min": 1e-5, "max": 1e-2}},
}
sweep_id = wandb.sweep(sweep_config, project="my_project")
wandb.agent(sweep_id, function=train_function, count=20)