hugging-face-trackio
GitHub用于机器学习训练实验的指标追踪与可视化。支持通过Python API记录日志，或通过CLI检索数据并同步至HF Spaces仪表盘，提供实时监控与JSON自动化输出。
Trigger Scenarios
Install
npx skills add synthetic-sciences/openscience --skill hugging-face-trackio -g -y
SKILL.md
Frontmatter
{
"name": "hugging-face-trackio",
"tags": [
"Hugging Face",
"Experiment Tracking",
"Logging",
"Metrics"
],
"author": "Synthetic Sciences",
"license": "Apache-2.0",
"version": "1.0.0",
"category": "other",
"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.",
"dependencies": [
"trackio",
"huggingface-hub"
]
}
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 |
| Retrieving metrics 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.
CLI → Retrieving
Use the trackio command to query logged metrics:
trackio list projects/runs/metrics— discover what's availabletrackio get project/run/metric— retrieve summaries and valuestrackio show— launch the dashboardtrackio sync— sync to HF Space
Key concept: Add --json for programmatic output suitable for automation and LLM agents.
→ See references/retrieving_metrics.md for all commands, workflows, and JSON output formats.
Minimal Logging Setup
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
trackio list projects --json
trackio get metric --project my-project --run my-run --metric loss --json
Version History
- e9844a4 Current 2026-07-11 17:31
Dependencies
-
required
trackio - required huggingface-hub
