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
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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
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</> SKILL.md
huggingface-trackio.SKILL.md
---name: huggingface-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 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](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. Auto-created Spaces are **public by default** — pass `private=True` if the metrics should not be public.
→ 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 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.
→ See [references/alerts.md](references/alerts.md) for the full alerts API, webhook setup, and autonomous agent workflows.
### CLI → Retrieving
Use the `trackio` command to query logged metrics and alerts:
- `trackio list projects/runs/metrics` — discover what's available
- `trackio get project/run/metric` — retrieve summaries and values
- `trackio list alerts --project <name> --json` — retrieve alerts
- `trackio show` — launch the dashboard
- `trackio sync` — sync to HF Space
**Key concept**: Add `--json` for programmatic output suitable for automation and LLM agents.
→ See [references/retrieving_metrics.md](references/retrieving_metrics.md) for all commands, workflows, and JSON output formats.
## Minimal Logging Setup
```python
import trackio
# Spaces are PUBLIC by default (good for shareable dashboards);
# pass private=True if the metrics should not be public
