ml-wandb-tracking

Track PyTorch Lightning training metrics with Weights & Biases by configuring the WandbLogger for your Trainer. This logs train and validation metrics alongside system stats during the run, keeping results tied to the exact configuration.

W&B experiment tracking centralizes metrics, hyperparameters, and artifacts for machine learning runs so experiments stay comparable and reproducible. You need it when you want to stop guessing which training configurations work and require traceability across runs.

Automate hyperparameter sweeps using W&B Sweeps to search over learning rates, batch sizes, and model options. This provides systematic tuning by defining search configurations that run iteratively to find optimal parameters.

You can version datasets and model checkpoints using W&B Artifacts, then use the W&B Model Registry to manage model lifecycle. This allows you to promote models across staging and production for deployment-ready versions.

You need to install the wandb library, initialize runs, and configure the WandbLogger to start logging experiment metrics. This setup applies to PyTorch Lightning training and workflows requiring reproducibility and traceability.