cadling — Installation

cadling requires Python 3.9+ and conda (for pythonocc-core and, on macOS, PyTorch).

Environment setup

# Create the conda environment (installs PyTorch via conda-forge)
conda env create -f environment.yml
conda activate cadling

# Install cadling in development mode
pip install -e ".[all]"

macOS: PyTorch must come from conda-forge

PyPI’s torch bundles a libomp.dylib that conflicts with conda’s OpenMP runtime, causing OMP: Error #15 crashes. Always install PyTorch (and the rest of the ML stack) from conda-forge — never pip install torch. See the monorepo Installation page.

Optional dependency groups

pip install -e ".[dev]"      # pytest, black, ruff, mypy, pre-commit
pip install -e ".[cad]"      # numpy-stl, trimesh, networkx
pip install -e ".[ml]"       # transformers (PyTorch via conda only)
pip install -e ".[vision]"   # transformers, easyocr, opencv-python
pip install -e ".[all]"      # Everything above

pythonocc-core

The BRep backend and STEP geometry use pythonocc-core, which is only available through conda (not PyPI) and is included in environment.yml. Tests that need it are marked requires_pythonocc and skip automatically when it is absent.

Neural integration (ll_stepnet)

cadling integrates ll_stepnet for STEP neural processing. It is installed as an editable dependency via environment.yml, or manually:

cd ../ll_stepnet
pip install -e .

Next

Continue to Usage.