weights-and-biases

Call wandb.init with your project name and config, then call wandb.log with metrics like loss and accuracy inside your training loop. Finish with wandb.finish, and optionally save checkpoints with wandb.save or as versioned artifacts.

Define a sweep config with a search method (grid, random, or bayes), a target metric, and parameter distributions, then create it with wandb.sweep. Launch one or more agents with wandb.agent pointing to your training function to run trials.

Yes, set report_to="wandb" in TrainingArguments and the Trainer automatically logs metrics, evaluation results, and checkpoints to W&B. You can also add custom WandbCallback subclasses for additional logging.

Artifacts are versioned files such as datasets or model checkpoints with automatic lineage tracking between runs. The model registry is a curated layer where model artifacts are linked and promoted through stages like staging and production using aliases.

Yes, set WANDB_MODE=offline before initializing your run and all metrics are stored locally. Later, run wandb sync on the run directory to upload the logged data to the W&B servers.

Use Bayesian optimization for expensive training runs with limited compute, since it learns from previous trials. Use random search for quick exploration of many parameters, and grid search only for a few discrete parameters needing exhaustive coverage.