hugging-face-trackio

ML experiment tracking is handled by logging metric values via a Python API or CLI during training to visualize them in real-time dashboards. You initialize with a project name, log metrics, and finish to start monitoring immediately.

Syncing ML metrics to Hugging Face Spaces is supported for persistent, shareable monitoring across runs and projects. You can optionally pass a space_id during initialization to enable remote Space synchronization alongside local dashboards.

Visualizing ML training metrics consistently across local and remote environments is achieved by logging values locally and optionally syncing to a remote Space. This approach ensures metrics remain accessible and shareable across different runs and projects.

You do not need to use Python exclusively, as Trackio covers both CLI and Python API workflows for logging metrics. This allows you to log metric values during training and visualize them in dashboards using either interface.

To initialize experiment tracking, you start with a project name and an optional space_id. Once initialized, you can log metric values via Python or CLI to start monitoring your ML training runs in real-time dashboards immediately.