From atum-ai-ml
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.
npx claudepluginhub arnwaldn/atum-plugins-collection --plugin atum-ai-mlThis skill uses the workspace's default tool permissions.
Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards.
Executes pre-written implementation plans: critically reviews, follows bite-sized steps exactly, runs verifications, tracks progress with checkpoints, uses git worktrees, stops on blockers.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Guides idea refinement into designs: explores context, asks questions one-by-one, proposes approaches, presents sections for approval, writes/review specs before coding.
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/logging_metrics.md |
| Retrieving metrics after/during training | CLI | references/retrieving_metrics.md |
Use import trackio in your training scripts to log metrics:
trackio.init()trackio.log() or use TRL's report_to="trackio"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.
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 SpaceKey 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.
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()
trackio list projects --json
trackio get metric --project my-project --run my-run --metric loss --json