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
