build: the agent installs one CUDA-13 stack instead of two, the web image drops ML packages it never imported, and Redis moves to 8 (1451, 1452)

Agent:
- The image ran PyPI's CUDA-13 torch 2.14 and onnxruntime-gpu 1.30 on a
  CUDA 12.9 cudnn-runtime base. requirements.txt had silently replaced the
  Dockerfile's torch 2.6+cu124, because ultralytics pulls torchvision, which
  pulls its own torch. That left ~3 GB of base libraries and a ~3 GB torch
  nothing loaded: 10 GB compressed.
- Now: an nvidia/cuda 13.0.3 `base` image, with torch and torchvision
  installed together from cu130. CUDA and cuDNN come from the nvidia-* pip
  packages; onnxruntime-gpu declares its [cuda,cudnn] extras.
- fc_agent/accel.py preloads those libraries for onnxruntime. It then logs,
  and reports in /status, whether torch and the ONNX CUDA provider actually
  got the GPU, since both fall back to the CPU silently.

Web image:
- Drop opencv-python-headless and onnxruntime, plus the opencv-only apt libs.
  Both have been listed since the scaffold and nothing in backend/ imports
  them.
- torch/torchvision move to 2.14/0.29, and the unexplained caps are lifted
  (rule 154).

Redis: 8-alpine in both compose files and both CI service containers. That
gives an AGPLv3 licence option, where 7.4 was RSAL/SSPL only. The client
moves to >=8.1.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR

agent/fc_agent/accel.py

@@ -0,0 +1,86 @@
+"""Which accelerator each runtime actually got — reported once, at startup.
+
+The agent has two GPU runtimes and both fall back to the CPU without raising:
+torch when the driver is too old for its CUDA build, and onnxruntime (the imgutils
+detector + CCIP models) when its CUDA provider cannot load its libraries. A
+fallback shows up only as slower work, and nothing reported it. On 2026-09-24 the
+image turned out to be running a CUDA-13 torch and onnxruntime on a CUDA-12 base
+(#1451), and whether the ONNX half was on the GPU could not be answered from
+anything the agent had ever logged.
+
+Also the fix for the likeliest way the ONNX half misses: onnxruntime-gpu's CUDA
+provider finds libcudart/cuBLAS/cuDNN only on the loader path, and in this image
+they live in the `nvidia-*` pip packages torch installs. `preload_dlls()` (ORT
+1.21+) loads them from there, so the provider resolves them by soname.
+
+Stdlib-only at import, so the unit suite can import it — torch and onnxruntime
+are imported inside the functions.
+"""
+
+from __future__ import annotations
+
+import ctypes
+import importlib
+import logging
+from pathlib import Path
+
+log = logging.getLogger("fc_agent.accel")
+
+# Filled by report(); /status carries it so the page can show it too.
+LAST: dict = {}
+
+
+def torch_status(imp=importlib.import_module) -> dict:
+    try:
+        torch = imp("torch")
+    except Exception as e:
+        return {"device": "unavailable", "error": str(e)}
+    out = {"version": torch.__version__, "cuda_build": torch.version.cuda}
+    if torch.cuda.is_available():
+        out["device"] = "cuda"
+        out["gpu"] = torch.cuda.get_device_name(0)
+    else:
+        out["device"] = "cpu"
+    return out
+
+
+def onnx_status(imp=importlib.import_module, load=ctypes.CDLL) -> dict:
+    try:
+        ort = imp("onnxruntime")
+    except Exception as e:
+        return {"device": "unavailable", "error": str(e)}
+    out = {"version": ort.__version__, "providers": list(ort.get_available_providers())}
+    if "CUDAExecutionProvider" not in out["providers"]:
+        out["device"] = "cpu"
+        return out
+    preload = getattr(ort, "preload_dlls", None)
+    if preload is not None:
+        try:
+            preload()
+        except Exception as e:
+            out["preload_error"] = str(e)
+    # "Available" only means the build HAS the provider. Loading its library is
+    # what resolves libcudart/cuBLAS/cuDNN — the step that fails when they are
+    # missing, and the one a session would otherwise fail silently on.
+    capi = Path(ort.__file__).parent / "capi"
+    try:
+        load(str(capi / "libonnxruntime_providers_shared.so"), mode=ctypes.RTLD_GLOBAL)
+        load(str(capi / "libonnxruntime_providers_cuda.so"))
+    except OSError as e:
+        out["device"] = "cpu"
+        out["error"] = str(e)
+    else:
+        out["device"] = "cuda"
+    return out
+
+
+def report() -> dict:
+    """Check both runtimes, log the result, and keep it for /status."""
+    LAST.clear()
+    LAST.update(torch=torch_status(), onnx=onnx_status())
+    for name, s in LAST.items():
+        if s.get("device") == "cuda":
+            log.info("accel: %s on GPU (%s)", name, s)
+        else:
+            log.warning("accel: %s is NOT on the GPU — work runs on the CPU (%s)", name, s)
+    return dict(LAST)