ll_brepnet — Installation

ll_brepnet needs PyTorch, pythonocc-core, and occwl, which on macOS must come from conda to avoid the OpenMP/libomp conflict (see the repo CLAUDE.md). It also uses pytorch-lightning for training and cadling for the shared B-Rep extraction machinery.

Option 1 — reuse the cadling environment (recommended)

The cadling conda environment already provides PyTorch, pythonocc-core, and occwl. Add the training deps and install the package editable:

conda activate cadling
pip install pytorch-lightning tensorboard
pip install -e ./ll_brepnet

Option 2 — standalone environment

conda env create -f ll_brepnet/environment.yaml
conda activate ll-brepnet

Verify

python -c "import ll_brepnet; print(ll_brepnet.__version__)"
# 0.1.0

Run the test suite (skips automatically without pythonocc / torch):

pytest ll_brepnet/tests -q          # fast tests
pytest ll_brepnet/tests -q -m ""    # include the slow end-to-end test

Dependencies

Dependency	Source	Why
pytorch	conda-forge	model + training (conda only, OpenMP safety)
pythonocc-core	conda-forge	STEP loading + B-Rep traversal
occwl	pip	UV-grid sampling (uvgrid / ugrid)
pytorch-lightning, torchmetrics, tensorboard	pip	training loop, mIoU/accuracy, logging
cadling	editable (monorepo)	coedge extraction + reused encoder
scikit-learn	conda/pip	train/val/test splitting