huggingface-trackio

Topics in the source

• Trackio - Experiment Tracking for ML Training
• Three Interfaces
• When to Use Each
• Python API → Logging
• Python API → Alerts
• CLI → Retrieving
• Minimal Logging Setup
• Minimal Retrieval

Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards. | Task | Interface | Reference | |------|-----------|-----------| | Logging metrics during training | Python API | references/loggingmetrics.md | | Firing alerts for training diagnostics | Python API | references/alerts.md | | Retrieving metrics & alerts after/during training | CLI | references/retrievingmetrics.md | Use import trackio in your training scripts to log metrics: Initialize tracking with trackio.init() Log metrics with trackio.log() or use TRL's reportto="trackio" Finalize with trackio.finish() Key concept: For remote/cloud training, pass spaceid — metrics sync to a Space dashboard so they persist after the instance terminates. Auto-created Spaces are public by default — pass private=True if the metrics should not be public. → See references/loggingmetrics.md for setup, TRL integration, and configuration options. Insert trackio.alert() calls in training code to flag important events — like inserting print statements for debugging, but structured and queryable: trackio.alert(title="...", level=trackio.AlertLevel.

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