hugging-face-trackio
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
npx skills add majiayu000/claude-skill-registry --skill hugging-face-trackio-ihatesea69-hieunghi-ai-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. ## Two Interfaces | Task | Interface | Reference | |------|-----------|-----------| | **Logging metrics** during training | Python API | [references/logging_metrics.md](references/logging_metrics.md) | | **Retrieving metrics** 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. → See [references/logging_metrics.md](references/logging_metrics.md) for setup, TRL integration, and configuration options. ### CLI → Retrieving Use the `trackio` command to query logged metrics: - `trackio list projects/runs/metrics` — discover what's available -
- Two Interfaces
- When to Use Each
- Python API → Logging
- CLI → Retrieving
- Minimal Logging Setup
- Minimal Retrieval
trackio list projects --json trackio get metric --project my-project --run my-run --metric loss --json
What does the hugging-face-trackio skill do?
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill hugging-face-trackio-ihatesea69-hieunghi-ai-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.