hugging-face-trackio
Track ML experiments with real-time dashboards
Developer Setup
Setup & Installation
npx skills add https://github.com/huggingface/skills --skill hugging-face-trackionpx skills add https://github.com/huggingface/skills --skill hugging-face-trackioOverview
What This Skill Does
Trackio is an experiment tracking library for ML training. It logs metrics via a Python API, fires structured alerts for diagnostic conditions, and exposes a CLI for querying logged data. Metrics sync to a Hugging Face Space for real-time dashboard monitoring.
Application
When to use this Skill
- Configuring integration settings for custom agent workflows.
- Optimizing query execution and response latency in production.
- Developing clean, standard-compliant implementations for enterprise services.
- Troubleshooting connection timeouts and authentication handshakes.
- Monitoring API rate limits and execution pipelines programmatically.
Documentation
Show Skills.md file
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 |
| Firing alerts for training diagnostics | Python API | references/alerts.md |
| Retrieving metrics & alerts after/during training | CLI | 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'sreport_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 for setup, TRL integration, and configuration options.
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