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/faces.py

@@ -71,11 +71,9 @@ class YuNetSFaceProcessor(FaceProcessor):
         logger.info("YuNet face detector loaded via OpenCV")
 
         # SFace via ONNX Runtime
-        opts = ort.SessionOptions()
-        opts.inter_op_num_threads = 2
-        opts.intra_op_num_threads = 2
+        from app.services.vision.providers import create_session
         ort.set_default_logger_severity(3)
-        self._recognizer = ort.InferenceSession(sface_path, opts, providers=["CPUExecutionProvider"])
+        self._recognizer = create_session(sface_path, configured_providers=settings.execution_providers)
         logger.info("SFace recognizer loaded via ONNX Runtime")
 
         self._min_face_size = settings.faces.min_face_size