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