weights-and-biases

Track ML experiment metrics automatically by initializing a run with wandb.init and logging metrics during training to populate real-time dashboards. This handles config management and metric logging across runs and projects.

Run hyperparameter sweeps by applying optimization configurations across multiple initialized runs. This tracks sweep metrics automatically, visualizes results in dashboards, and manages datasets using artifact versioning.

Artifact versioning works by handling datasets and models through a centralized registry. You log artifacts during runs to manage dataset and model versioning for collaborative ML projects.

Yes, you can manage a model registry for team collaboration workflows. It provides shared workspaces and centralized registries to support governance across collaborative ML projects.

Yes, experiment tracking supports integration with common ML frameworks. You initialize a run and apply automatic logging across model training, evaluation, and hyperparameter sweep workflows.

To initialize a run and start logging metrics, you need to call wandb.init with your project name. This begins config management and metric logging for your training runs.