trackio-observability

name	trackio-observability
description	Use when instrumenting or inspecting TRL training runs with Trackio, run names, metric schemas, dashboards, logs, grep or ripgrep, SFTP, Hugging Face Job logs, remote artifacts, or experiment result summaries.

Trackio Observability

Use this skill to make training runs observable and debuggable.

Workflow

1. Define the run identity: project, run name, method, model, dataset, seed, and challenge.
2. Add Trackio. For any remote Hugging Face Job, initialize a hosted dashboard with trackio.init(..., space_id="owner/space"); only short local smoke tests may stay local or skip tracking with an explicit reason.
3. Make logs grep-friendly with clear phase markers.
4. Persist artifacts intentionally: model, adapter, config, metrics, traces, and evaluation outputs.
5. Inspect remote state with the narrowest tool: Trackio dashboard, HF CLI, rg, or SFTP when configured.

Reporting Shape

Return:

• run id or job id
• Trackio dashboard or Space
• command or script inspected
• latest metrics
• artifact paths
• failure signatures
• next minimal action

Never print tokens, secrets, private credentials, or full logs unless the user explicitly asks for a raw excerpt.

References

• references/tracking-schema.md: run metadata and metric schema.
• references/log-inspection.md: grep, SFTP, and remote artifact triage.