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hugging-face-trackio

Track and visualize ML training metrics with Trackio via Python API and CLI.

1|Updated Jan 22, 2026
Search Tags:#metrics#CLI#dashboard#Python#trackio#Space syncing#ML training
One-click install
npx skills add https://github.com/sitammeur/ml-agent-skills --skill hugging-face-trackio-sitammeur
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-trackio
Source: https://github.com/sitammeur/ml-agent-skills/tree/main/skills/hugging-face-trackio
Command: npx skills add https://github.com/sitammeur/ml-agent-skills --skill hugging-face-trackio-sitammeur

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the process of recording, aggregating, and visualizing ML training metrics, so teams can quickly monitor progress and compare runs without manual log analysis.

Core Features & Use Cases

  • Real-time logging and dashboarding: Capture metrics during training and display them in a live Trackio dashboard, with optional Space syncing for persistence.
  • CLI and Python API access: Use a simple Python API to log metrics and a CLI to list runs, fetch metrics, and launch dashboards for automation and LLM-driven workflows.
  • Use Case: Imagine you run multiple experiments; track learning curves, compare final metrics, and share dashboards across your team with minimal setup.

Quick Start

Install and configure Trackio integration, then initialize a run and start logging:

  • Import trackio and call trackio.init(project="my-project", space_id="username/trackio")
  • Log metrics during training with trackio.log({"loss": loss, "accuracy": acc})
  • View the dashboard with trackio.show() or trackio.sync(...)

Frequently Asked Questions about hugging-face-trackio

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track ML training metrics in real time?▼

Track ML training metrics in real time by initializing a run with trackio.init and logging values during training with trackio.log. You can view live updates via trackio.show and optionally sync to a Space for persistence.

Can I compare multiple ML training runs without manual log analysis?▼

Yes, you can compare ML training runs without manual log analysis by using Trackio. It aggregates metrics across runs, allowing you to compare learning curves and final metrics directly in the dashboard.

How do I list runs and fetch ML metrics from the command line?▼

List runs and fetch ML metrics from the command line using the Trackio CLI. The CLI supports listing runs, fetching metrics, and launching dashboards to automate workflows without writing Python code.

What's the best way to share an ML metrics dashboard with my team?▼

Share an ML metrics dashboard with your team by syncing your Trackio run to a Space using trackio.sync. This provides persistent dashboard access for team members with minimal setup required.

Do I need to install the Trackio library to log Python training metrics?▼

Yes, installing the Trackio library is required to log Python training metrics. The skill uses its Python API to capture metrics during training and the CLI to manage runs and launches dashboards.

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