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. ## Two Interfaces | Task | Interface | Reference | |------|-----------|-----------| | **Logging metrics** during training | Python API | [references/logging_metrics.md](references/logging_metrics.md) | | **Retrieving metrics** 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. ### 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](references/retrieving_metrics.md) for all commands, workflows, and JSON output formats. ## Minimal Logging Setup ```python 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 ```bash trackio list projects --json trackio get metric --project my-project --run my-run --metric loss --json ```