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/insightface_processor.py

@@ -23,10 +23,13 @@ class InsightFaceProcessor(FaceProcessor):
         model_root = str(Path(settings.models_dir) / "face" / "insightface")
         logger.info("Loading InsightFace buffalo_l from %s", model_root)
 
+        from app.services.vision.providers import get_providers
+        providers = get_providers(settings.execution_providers)
+
         self._app = FaceAnalysis(
             name="buffalo_l",
             root=model_root,
-            providers=["CPUExecutionProvider"],
+            providers=providers,
         )
         self._app.prepare(ctx_id=-1, det_size=(640, 640))
         self._min_det_score = settings.faces.recognition_threshold