القائمة
Deploy a trained YOLO model as a Nuclio serverless function for CVAT auto-annotation.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Set up autonomous training monitoring — creates cron jobs to track long-running training, auto-continue pipeline when training completes.
Audit YOLO dataset quality — class distribution, annotation quality, image stats, and improvement suggestions.
Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain.
Analyze YOLO training runs — compares to baseline/best, checks per-class regression, analyzes training dynamics and tune convergence, writes actionable recommendations.
Run autonomous YOLO training experiments — reads training-plan.md, assesses bottlenecks, acts strategically, and delegates HP optimization to model.tune().
Profile YOLO model inference speed, FPS, and size across image sizes and export formats.
| name | cvat-deploy |
| description | Deploy a trained YOLO model as a Nuclio serverless function for CVAT auto-annotation. |
Deploy a trained YOLO model as a Nuclio serverless function so CVAT users can auto-annotate images directly from the CVAT UI.
yolo-project.yaml has class definitionsexperiments/summary.md for the best-performing model pathyolo-cvat deploy --model <path_to_model> --name <detector_name>
This generates:
serverless/<name>/function.yaml — Nuclio config with class specserverless/<name>/main.py — Inference handlerserverless/<name>/best.onnx — Exported modelRead the generated function.yaml and confirm:
yolo-project.yamlProvide the command for the user to run:
nuctl deploy --path ./serverless/<name> --platform local
nuctl get functions
Check that the function is running.
Model is .pt format?
├── Yes → Export to ONNX first (yolo-export)
└── Already .onnx → Copy directly
Nuclio reachable on port 8070?
├── Yes → Ready to deploy
└── No → Warn user, suggest checking Docker and CVAT stack
Function with same name exists?
├── Yes → Ask user: overwrite or use different name?
└── No → Deploy normally