| name | wandb |
| description | Weights & Biases — ML experiment tracking and visualization. Log metrics, hyperparameters, model checkpoints, and artifacts. Collaborative dashboards, sweep hyperparameter search, and model registry. |
| tags | ["experiment-tracking","hyperparameter-sweeps","model-observability","training-visualization","wandb"] |
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
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)
References
