Huggingface Trackio
Track and visualize ML training experiments with Trackio.
Install
npx skills add huggingface/skills --skill huggingface-trackio -g -a claude-code -y- Repository
- huggingface/skills11.2k
- Category
- Data and ML
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,181 tokens
- Runs scripts
- No, instructions only
What it does
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
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 |
| Firing alerts for training diagnostics | Python API | references/alerts.md |
| Retrieving metrics & alerts after/during training | CLI | 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'sreport_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. Auto-created Spaces are public by default, pass private=True if the metrics should not be public.
→ See 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 are printed to terminal, stored in the database, shown in the dashboard, and optionally sent to webhooks (Slack/Discord)
