bvandeusenandClaude Opus 5.5
7a09dc3cda
CI and images / lint (push) Successful in 3s
CI and images / extension-version (push) Successful in 3s
extension / lint (push) Successful in 16s
CI and images / frontend-build (push) Successful in 19s
CI and images / backend-lint-and-test (push) Successful in 31s
CI and images / integration (push) Successful in 2m22s
CI and images / sign-extension (push) Successful in 3s
CI and images / build-web (push) Successful in 3m7s
CI and images / smoke-web (push) Successful in 59s
CI and images / build-agent (push) Successful in 6m41s
CI and images / promote (push) Successful in 2s
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
29 lines
1.3 KiB
Plaintext
29 lines
1.3 KiB
Plaintext
-r requirements.txt
|
|
|
|
# ML stack — versions current as of 2026-05-14 with Python 3.14 wheel coverage.
|
|
|
|
# torch + torchvision are NOT listed here: they are installed CPU-only from
|
|
# the PyTorch CPU index in Dockerfile. The default PyPI torch wheel bundles
|
|
# the NVIDIA CUDA runtime (a ~5.6GB image layer); this pipeline is CPU-only,
|
|
# so Dockerfile uses the +cpu wheels from
|
|
# https://download.pytorch.org/whl/cpu instead.
|
|
#
|
|
# 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
|
|
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
|