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

requirements-ml.txt

@@ -8,21 +8,21 @@
 # so Dockerfile uses the +cpu wheels from
 # https://download.pytorch.org/whl/cpu instead.
 #
-# IMPORTANT: torchvision 0.27 declares requires_python "!=3.14.1,>=3.10" —
-# Python 3.14.1 specifically is excluded due to a known incompatibility.
-# The python-ci runner pulls python:3.14-bookworm (latest patch); if that
-# resolves to 3.14.1 the install will fail. Pin a specific Python patch in
-# the runner image (CI-Runner/CI-python/Dockerfile) if this becomes a
-# blocker. 3.14.0 and 3.14.2+ are fine.
+# torchvision declares requires_python "!=3.14.1" (0.27 through 0.29). The
+# image's python:3.14-slim is past that patch, so it only bites a build pinned
+# to exactly 3.14.1.
+#
+# No caps below: rule 154 wants a named breakage for one, and none of the
+# `<N` caps these lines used to carry had one. opencv-python-headless and
+# onnxruntime were dropped (#1451): listed since the 2026-05-14 scaffold,
+# imported by nothing in backend/ — ONNX inference lives in the GPU agent.
 
-transformers>=5.8,<6.0
-onnxruntime>=1.26,<2.0
-huggingface-hub>=1.14,<2.0
-opencv-python-headless>=4.13,<5.0
+transformers>=5.8
+huggingface-hub>=1.14
 
 # scikit-learn powers the tag-eval (#1130) head-vs-centroid comparison: logistic
 # regression + cross-validated precision/recall/AP. Battle-tested metrics matter
 # because that eval's whole purpose is producing trustworthy numbers. numpy is
 # left to resolve transitively (torch/transformers/sklearn all pull it) to avoid
 # pinning against their constraints.
-scikit-learn>=1.7,<2.0
+scikit-learn>=1.7