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Track and visualize ML training experiments with Trackio.
Passed 24 of 24 test cases with the skill, and 4 of 24 without it.
and 34 more in the full report
20 fixed4 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 20 test cases the skill fixed. Same model, same prompt, with and without the skill.
Provide a minimal Python script snippet to initialize experiment logging under project 'demo', log a dictionary with loss 0.1, and cleanly close the experiment session.
| Checked | Without skill | With skill |
|---|---|---|
| Calls trackio.init with project='demo'. | Missed | Passed |
| Calls trackio.log with dictionary payload. | Missed | Passed |
| Calls trackio.finish to finalize tracking. | Missed | Passed |
The instructions your model reads when the skill loads.
Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards.
| 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 |
Use import trackio in your training scripts to log metrics:
trackio.init()trackio.log() or use TRL's report_to="trackio"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.
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 alertINFO, WARN, ERRORKey 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 for the full alerts API, webhook setup, and autonomous agent workflows.
test cases passed, vs 4 / 24 without
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