hugging-face-trackiolisted

# Trackio - Experiment Tracking for ML Training Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards. ## Three Interfaces | Task | Interface | Reference | |------|-----------|-----------| | **Logging metrics** during training | Python API | [references/logging_metrics.md](references/logging_metrics.md) | | **Firing alerts** for training diagnostics | Python API | [references/alerts.md](references/alerts.md) | | **Retrieving metrics & alerts** after/during training | CLI | [references/retrieving_metrics.md](references/retrieving_metrics.md) | ## When to Use Each ### Python API → Logging Use `import trackio` in your training scripts to log metrics: - Initialize tracking with `trackio.init()` - Log metrics with `trackio.log()` or use TRL's `report_to="trackio"` - Finalize with `trackio.finish()` **Key concept**: For remote/cloud training, pass `space_id` — metrics sync to a Space dashboard so they persist after the instance terminates. → See [references/logging_metrics.md](references/logging_metrics.md) for setup, TRL integration, and configuration options. ### Python API → Alerts Insert `trackio.alert()` calls in training code to flag important events — like inserting print statements for debugging, but structured and queryable: - `trackio.alert(title="...", level=trackio.AlertLevel.WARN)` — fire an alert - Three severity levels: `INFO`, `WARN`, `ERROR` - Alerts