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RTSPanda v0.0.8

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@248Tech 248Tech released this 19 Mar 19:43
· 12 commits to master since this release
v0.0.8
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RTSPanda v0.0.8 Release Notes

Headline

RTSPanda v0.0.8 turns the project into a more serious edge-video platform: the default single-machine workflow stays intact, while Raspberry Pi deployments gain a cleaner first-run path, deterministic ONNX-only AI builds, and an easy upgrade path to distributed inference.

Highlights

  • Added a Pi-first lightweight deployment mode for rtspanda without local AI-worker overhead.
  • Added cluster mode so a Pi can ingest streams locally and send frames to a remote AI worker on a second machine.
  • Removed AI-worker export fallback from Docker builds. The worker now uses prebuilt ONNX models only.
  • Added additive Compose profiles for full, pi, and ai-worker without breaking the existing docker compose up --build -d flow.
  • Rewrote setup and deployment docs to clearly support Standard, Pi Standalone, and Pi + AI topologies.
  • Fixed Pi standalone startup so it no longer traverses the local AI-worker build path.
  • Repaired the default remote ONNX pin to a live Ultralytics asset and improved failure messages for invalid model sources.

What Changed

Platform and Deployment

  • docker-compose.yml now supports:
    • standard full-stack deployment
    • lightweight Pi deployment
    • standalone remote AI-worker deployment
  • New docker-compose.standalone.yml overlay isolates Pi-only and AI-worker-only deployments from the unprofiled full stack.
  • scripts/pi-up.sh now supports:
    • PI_DEPLOYMENT_MODE=pi
    • PI_DEPLOYMENT_MODE=full
    • PI_DEPLOYMENT_MODE=ai-worker
  • Standalone launch paths now use build followed by up --no-build so targeted deployments only build the intended service.
  • scripts/pi-preflight.sh now checks deployment mode and model-source expectations more accurately for Docker-first Pi workflows.

AI Runtime

  • ai_worker/Dockerfile now resolves models deterministically:
    • local prebuilt ONNX file first
    • explicit YOLO_MODEL_URL second
    • named Ultralytics ONNX asset fallback last
  • Default remote model pin now targets yolo11n on Ultralytics v8.3.0, which is published and buildable today.
  • No PyTorch install path
  • No YOLO(...).export(...)
  • No runtime model conversion on ARM

Backend Detection Routing

  • Added additive AI-target resolution using:
    • AI_MODE=local|remote
    • AI_WORKER_URL=http://<host>:8090
    • DETECTOR_URL as the highest-precedence override
  • Detection health now reports AI mode and remote worker context.

Release Quality Improvements

  • Refreshed the frontend lockfile so current tests/tooling install cleanly.
  • Fixed clean-checkout frontend embed compilation behavior in the backend.

Setup Paths

Standard

docker compose up --build -d

Pi Standalone

./scripts/pi-up.sh

Pi + AI

AI host:

docker compose -f docker-compose.yml -f docker-compose.standalone.yml --profile ai-worker build ai-worker-standalone
docker compose -f docker-compose.yml -f docker-compose.standalone.yml --profile ai-worker up -d --no-build ai-worker-standalone

Pi host:

export AI_WORKER_URL="http://192.168.1.50:8090"
./scripts/pi-up.sh

Validation Checklist

  • docker compose config -q
  • cd backend && go test ./internal/...
  • cd frontend && npm run test -- --config vitest.config.ts
  • cd ai_worker && python -m pytest -q

Upgrade Notes

  • Existing standard users can continue using docker compose up --build -d.
  • Pi users who want the old all-in-one behavior can use PI_DEPLOYMENT_MODE=full ./scripts/pi-up.sh.
  • For custom ONNX assets, place model.onnx at repo root or ai_worker/model/model.onnx, then set MODEL_SOURCE=local.
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