feat: add GPU/CUDA acceleration support (:cuda Docker tag)

Add a :cuda Docker image tag that auto-detects NVIDIA GPU at runtime
and falls back gracefully to CPU. Same pattern as Immich.

- New gpu.py shared utility for cached CUDA detection
- Background removal (rembg): pass CUDAExecutionProvider to ONNX Runtime
- Upscaling (Real-ESRGAN): use CUDA device + FP16 when GPU available
- OCR (PaddleOCR): enable use_gpu when CUDA detected
- Dispatcher reports GPU status at startup via readiness signal
- Admin health endpoint exposes GPU availability
- Dockerfile uses ARG GPU=false with conditional NVIDIA CUDA base image
- docker-compose.gpu.yml override for GPU users
- CI/CD workflows build and publish :cuda tag (amd64 only)

Three tags: :latest (CPU), :lite (no AI), :cuda (GPU with CPU fallback)

packages/ai/python/gpu.py

@@ -0,0 +1,34 @@
+"""Runtime GPU/CUDA detection utility."""
+import functools
+import os
+
+
+@functools.lru_cache(maxsize=1)
+def gpu_available():
+    """Return True if a usable CUDA GPU is present at runtime."""
+    override = os.environ.get("STIRLING_GPU")
+    if override is not None:
+        return override.lower() in ("1", "true", "yes")
+
+    try:
+        import onnxruntime
+        if "CUDAExecutionProvider" in onnxruntime.get_available_providers():
+            return True
+    except ImportError:
+        pass
+
+    try:
+        import torch
+        if torch.cuda.is_available():
+            return True
+    except ImportError:
+        pass
+
+    return False
+
+
+def onnx_providers():
+    """Return ONNX Runtime execution providers in priority order."""
+    if gpu_available():
+        return ["CUDAExecutionProvider", "CPUExecutionProvider"]
+    return ["CPUExecutionProvider"]