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