weights-and-biases

Track machine learning experiments by initializing W&B in your training script and logging losses, accuracies, and system stats during each epoch. This centralizes metrics and configuration values for every run across frameworks like PyTorch and TensorFlow.

Run hyperparameter sweeps by coordinating searches with grid, random, or Bayesian strategies. W&B automatically compares outcomes across runs, allowing you to evaluate validation curves and identify the best performing model configurations.

Version datasets and manage your model registry by saving checkpoints as artifacts with lineage and deployment aliases. This ensures run history and model versions stay reproducible for collaborative MLOps reporting.

Experiment tracking works with HuggingFace and Lightning through direct framework integrations. You can plug W&B into these training loops to automatically log metrics and system stats without heavily modifying your custom code.

You need artifact versioning when saving final models or checkpoints to maintain reproducible run history. It tracks dataset and model lineage, allowing research teams to promote specific checkpoints to the registry and share reproducible run links with collaborators.

Start using W&B by initializing it in your training script, logging metrics during epochs, and saving your final model as an artifact. This ensures your run history and model versions stay reproducible in a single workspace.