feat: GPU acceleration support for ONNX Runtime inference

Centralize execution provider selection in providers.py with
auto-detection and graceful fallback. All ONNX sessions (embedder,
detector, face processor, recognizer) now use the configured providers.

- New VISION_EXECUTION_PROVIDERS env var: "auto" for GPU auto-detect,
  or explicit "CUDAExecutionProvider,CPUExecutionProvider"
- Provider priority: CUDA > ROCm > OpenVINO > CPU (when set to "auto")
- docker-compose.yml includes commented-out NVIDIA GPU deploy section
- Supports onnxruntime-gpu as a drop-in replacement for onnxruntime

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/services/vision/detect.py

@@ -120,12 +120,10 @@ class YOLOv8Detector(ObjectDetector):
     def __init__(self, settings: VisionSettings):
         model_path = Path(settings.models_dir) / "detect" / "yolov8n.onnx"
 
-        opts = ort.SessionOptions()
-        opts.inter_op_num_threads = 2
-        opts.intra_op_num_threads = 2
+        from app.services.vision.providers import create_session
 
         logger.info("Loading YOLOv8n from %s", model_path)
-        self._session = ort.InferenceSession(str(model_path), opts, providers=["CPUExecutionProvider"])
+        self._session = create_session(str(model_path), configured_providers=settings.execution_providers)
         self._conf_threshold = settings.detector.min_confidence
         self._max_detections = settings.detector.max_detections