# Huggingface Trackio

Track and visualize ML training experiments with Trackio.

- Repository: https://github.com/huggingface/skills (11249 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add huggingface/skills --skill huggingface-trackio -g -a claude-code -y`
Codex: `npx skills add huggingface/skills --skill huggingface-trackio -g -a codex -y`
Cursor: `npx skills add huggingface/skills --skill huggingface-trackio -g -a cursor -y`
Gemini CLI: `npx skills add huggingface/skills --skill huggingface-trackio -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add huggingface/skills`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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)

The rest: https://github.com/huggingface/skills/blob/HEAD/skills/huggingface-trackio/SKILL.md