| name | hugging-face-trackio |
| description | 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. |
| category | other |
| version | 1.0.0 |
| author | Synthetic Sciences |
| license | Apache-2.0 |
| tags | ["Hugging Face","Experiment Tracking","Logging","Metrics"] |
| dependencies | ["trackio","huggingface-hub"] |
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
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 availabletrackio get project/run/metric — retrieve summaries and valuestrackio show — launch the dashboardtrackio 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
