| name | hugging-face-trackio |
| description | 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... |
| risk | unknown |
| source | https://github.com/huggingface/skills/tree/main/skills/huggingface-trackio |
| source_repo | huggingface/skills |
| source_type | official |
| date_added | "2026-07-01T00:00:00.000Z" |
| license | Apache-2.0 |
| license_source | https://github.com/huggingface/skills/blob/main/LICENSE |
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
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 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)
Key concept for LLM agents: Alerts are the primary mechanism for autonomous experiment iteration. An agent should insert alerts into training code for diagnostic conditions (loss spikes, NaN gradients, low accuracy, training stalls). Since alerts are printed to the terminal, an agent that is watching the training script's output will see them automatically. For background or detached runs, the agent can poll via CLI instead.
