# Patchy631 AI Engineering Hub Hugging Face Trackio

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- Skill: `tomevault-io/patchy631-ai-engineering-hub-hugging-face-trackio` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/patchy631-ai-engineering-hub-hugging-face-trackio`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/patchy631-ai-engineering-hub-hugging-face-trackio/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/patchy631-ai-engineering-hub-hugging-face-trackio

---

---
name: hugging-face-trackio
description: 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.
---

# 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
- `trackio get project/run/metric` — retrieve summaries and values
- `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

trackio.init(project="my-project", space_id="username/trackio")
trackio.log({"loss": 0.1, "accuracy": 0.9})
trackio.log({"loss": 0.09, "accuracy": 0.91})
trackio.finish()
```

### Minimal Retrieval

```bash
trackio list projects --json
trackio get metric --project my-project --run my-run --metric loss --json
```

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<!-- tomevault:4.0:skill_md:2026-04-11 -->