It reported "onnx on GPU" beside torch failing cuInit with "CUDA unknown
error". Every library resolved, but no device could be used. The check now
calls cudaGetDeviceCount and reports the CUDA error when there is one.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR
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