Installation

The LatticeLabs Toolkit is a monorepo. You can install everything at once through the conda environment, or install individual packages with pip.

Prerequisites

• Python 3.9 – 3.12
• Conda (Miniconda or Miniforge recommended)

macOS-critical: install PyTorch via conda-forge

Do not pip install torch

PyTorch must be installed via conda-forge, not pip. PyPI’s torch bundles its own libomp.dylib, which conflicts with conda’s llvm-openmp and crashes the process on macOS:

OMP: Error #15: Initializing libomp.dylib, but found libomp.dylib already initialized.

Always install PyTorch (and torchvision, torchaudio, pytorch-geometric) from conda-forge. The environment.yml is the authoritative dependency source.

Full monorepo (recommended)

# Clone the repository
git clone https://github.com/LatticeLabsAI/ll_toolkit.git
cd ll_toolkit

# Create the conda environment (installs PyTorch, pythonocc, and all packages)
conda env create -f environment.yml
conda activate cadling

The environment installs cadling, ll_stepnet, and geotoken as editable packages.

Individual packages

Each package is independently installable with pip (run after activating the conda environment so PyTorch is already present):

pip install -e ./cadling          # CAD document processing
pip install -e ./ll_stepnet       # STEP/BRep neural networks
pip install -e ./geotoken         # Geometric tokenizer
pip install -e ./ll_ocadr         # Optical CAD recognition
pip install -e ./ll_gen           # Generation orchestration
pip install -e ./ll_clouds        # Point-cloud processing

Optional dependency groups

The root pyproject.toml defines extras you can install on top of the base packages:

pip install -e ".[dev]"        # Testing, linting, docs
pip install -e ".[cad]"        # CAD processing (trimesh, networkx, numpy-stl)
pip install -e ".[ml]"         # ML (transformers, accelerate, einops)
pip install -e ".[vision]"     # Vision (opencv, easyocr, matplotlib)
pip install -e ".[hub]"        # HuggingFace Hub integration
pip install -e ".[drawings]"   # 2D drawings (DXF, PDF)
pip install -e ".[all]"        # Everything

pythonocc-core

pythonocc-core (used by cadling’s BRep backend and STEP geometry) is only available through conda, not PyPI. It is included in environment.yml.

Native MLX (Apple Silicon)

The neural models also train and run natively in MLX on Apple Silicon (M-series). Each package’s mlx/ trainers convert the real PyTorch checkpoints and verify parity, or train from scratch — see each package’s Usage page.

pip install mlx mlx-lm   # in the conda env; mlx-lm is used by ll_ocadr's LLM splice

The OCC kernel (pythonocc-core) is still required for the generation packages, since validity is measured by executing generated programs through the real kernel.

Next steps

Continue to the Quickstart for a first end-to-end run, or jump to any package’s Overview from the sidebar.