feat: migrate Coral TPU skill from pycoral to ai-edge-litert (LiteRT)#180
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feat: migrate Coral TPU skill from pycoral to ai-edge-litert (LiteRT)#180
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Breaking change: Docker is no longer the default deployment — native venv is.
detect.py:
- Replace pycoral + tflite-runtime with ai-edge-litert
- Use litert.Interpreter + load_delegate('libedgetpu') for Edge TPU
- Platform-aware delegate lib name (Linux/macOS/Windows)
- Unified output tensor parsing (removed separate pycoral path)
- Add 'runtime' field to ready event
requirements.txt:
- pycoral~=2.0 + tflite-runtime~=2.14 → ai-edge-litert>=2.1.0
- Python 3.9-3.13 supported (was 3.6-3.9)
deploy.sh:
- Docker build → native Python venv
- Auto-installs libedgetpu via apt on Debian/Ubuntu
- Install instructions for macOS and Windows
- Native run_command (no docker run)
deploy.bat:
- Docker → native Python venv for Windows
tpu_probe.py:
- list_edge_tpus() → load_delegate() availability test
SKILL.md:
- Version 1.0.0 → 2.0.0
- Removed runtime: docker
- Updated Platform Setup with libedgetpu install commands
- Docker noted as optional alternative
Dockerfile:
- Marked as optional, bullseye-slim → bookworm-slim
- pycoral → ai-edge-litert
…tion skills - Remove Docker references from Coral TPU and OpenVINO table entries - Update mermaid diagram to show all 3 detection skills as native - Show Coral TPU using ai-edge-litert + libedgetpu delegate - Show OpenVINO using OpenVINO SDK with NCS2/iGPU/CPU targets - Replace 'docker build' with 'system packages' in LLM install step
…till uses Docker)
The skill crashed with 'No Edge TPU model found' because deploy.sh never downloaded a .tflite model. Now downloads SSD MobileNet V2 (Edge TPU compiled) from google-coral/test_data during deployment, plus a CPU-only fallback variant.
Ships the model in-repo so the skill works immediately after deployment without requiring an internet download step. Force-added past .gitignore *.tflite rule.
The _edgetpu.tflite model uses custom Edge TPU ops that can't run on CPU. When no Coral USB is connected, the fallback needs the standard quantized model without edgetpu-custom-op.
…B passthrough This reverts the deployment logic to the Docker-first approach originally introduced in commit 9137400 to bypass sudo and libedgetpu installation hurdles on macOS. The compiled tflite models added in recent commits remain.
Aligns the yolo-detection-2026-coral-tpu skill architectural flow with CameraClaw by using a host-side `monitor.js` script rather than emitting a hardcoded `run_command` from `deploy.sh`. This fixes Docker invocation inconsistencies natively inside Aegis. Also handles cross-platform volume mapping via `os.tmpdir()` which fixes Windows mapping issues.
… path in monitor wrapper
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Breaking change: Docker is no longer the default deployment — native venv is.
detect.py:
requirements.txt:
deploy.sh:
deploy.bat:
tpu_probe.py:
SKILL.md:
Dockerfile: