hugging-face-trackio

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](references/logging_metrics.md)
Firing alerts for training diagnostics	Python API	[references/alerts.md](references/alerts.md)
Retrieving metrics & alerts after/during training	CLI	[references/retrieving_metrics.md](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](references/logging_metrics.md) for setup, TRL integration, and configuration options.

Python API → Alerts

Insert trackio.alert() calls in training code to flag important events — like inserting print statements for debugging, but structured and queryable:

• `trackio.alert(title="...", leve