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/Dockerfile

@@ -1,10 +1,21 @@
 # FabledCurator GPU agent — runs on the desktop with the GPU.
-# CUDA 12.9 + cuDNN 9 runtime so onnxruntime-gpu can use the card (it needs
-# cuDNN 9 — the plain -runtime image lacks it: "libcudnn.so.9: cannot open
-# shared object file"); ffmpeg for video frames. Ubuntu 24.04 → Python 3.12.
-# Stays on the CUDA-12 / cuDNN-9 line the default onnxruntime-gpu + torch are
-# built against (CUDA 13 has only nascent ONNX Runtime support).
-FROM nvidia/cuda:12.9.2-cudnn-runtime-ubuntu24.04
+#
+# The `base` flavour, not `cudnn-runtime`: CUDA and cuDNN arrive as the
+# `nvidia-*` pip packages torch and onnxruntime-gpu depend on, so the base only
+# has to hand the container the driver (it sets NVIDIA_VISIBLE_DEVICES /
+# NVIDIA_DRIVER_CAPABILITIES for the Container Toolkit). Until #1451 this was
+# `12.9.2-cudnn-runtime` under a `torch==2.6.0+cu124` — and requirements.txt then
+# REPLACED that torch with PyPI's CUDA-13 build (ultralytics pulls torchvision,
+# which pulls its matching torch), beside a CUDA-13 onnxruntime-gpu. The image
+# ran CUDA 13 on a CUDA-12 base, carrying ~3 GB of base libraries and a ~3 GB
+# torch nothing loaded: 10 GB compressed.
+#
+# 13.0 because that is the line both wheels are built for (torch's cu130 index,
+# onnxruntime-gpu's `nvidia-cuda-runtime~=13.0`). Needs an NVIDIA driver that
+# supports CUDA 13 (580+); fc_agent/accel.py logs at startup whether torch and
+# onnxruntime actually got the GPU, since both fall back to the CPU silently.
+# ffmpeg for video frames. Ubuntu 24.04 → Python 3.12.
+FROM nvidia/cuda:13.0.3-base-ubuntu24.04
 
 # PIP_BREAK_SYSTEM_PACKAGES: Ubuntu 24.04 marks its system Python as externally
 # managed (PEP 668), so a global `pip install` errors without this. It's a
@@ -16,10 +27,12 @@ RUN apt-get update \
     && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
-# torch from the CUDA-12.4 wheel index; its wheels bundle their own CUDA + cuDNN
-# so they run on the 12.9 base and coexist with onnxruntime-gpu. Installed first
-# + separately so the GPU build of torch is deterministic and layer-cached.
-RUN pip3 install --no-cache-dir torch==2.6.0 --index-url https://download.pytorch.org/whl/cu124
+# torch AND torchvision from the cu130 index, together and first. Installing
+# torch alone is what let the next step swap it out: ultralytics needs
+# torchvision, PyPI's torchvision pins its own torch, and pip replaced ours to
+# match. With both present, requirements.txt finds them satisfied.
+RUN pip3 install --no-cache-dir --index-url https://download.pytorch.org/whl/cu130 \
+        torch torchvision
 COPY requirements.txt .
 RUN pip3 install --no-cache-dir -r requirements.txt
 COPY fc_agent ./fc_agent