hugging-face-trackio
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Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
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npx skill4agent add patchy631/ai-engineering-hub hugging-face-trackioSKILL.md Content
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 in your training scripts to log metrics:
import trackio- Initialize tracking with
trackio.init() - Log metrics with or use TRL's
trackio.log()report_to="trackio" - Finalize with
trackio.finish()
Key concept: For remote/cloud training, pass — metrics sync to a Space dashboard so they persist after the instance terminates.
space_id→ See references/logging_metrics.md for setup, TRL integration, and configuration options.
CLI → Retrieving
Use the command to query logged metrics:
trackio- — discover what's available
trackio list projects/runs/metrics - — retrieve summaries and values
trackio get project/run/metric - — launch the dashboard
trackio show - — sync to HF Space
trackio sync
Key concept: Add for programmatic output suitable for automation and LLM agents.
--json→ See 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