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
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'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 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 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