Installation

Kaolin is written with PyTorch and uses C++ / CUDA for efficient custom ops.

Requirements

  • Linux, macOS (CPU-only) or Windows

  • Python >= 3.7, <= 3.9

  • CUDA >= 10.0 (with ‘nvcc’ installed)

Dependencies

  • torch >= 1.5, <= 1.11.0

  • cython == 0.29.20 (auto-installed)

  • scipy >= 1.2.0 (auto-installed)

  • Pillow >= 8.0.0 (auto-installed)

  • usd-core >= 20.11 (auto-installed; required for USD I/O and 3D checkpoints with Timelapse)

Installation from source

Note

We recommend installing Kaolin into a virtual environment, for instance with Anaconda:

$ conda create --name kaolin python=3.7
$ conda activate kaolin

1. Clone Repository

Clone and optionally check out an official release:

$ git clone --recursive https://github.com/NVIDIAGameWorks/kaolin
$ cd kaolin
$ git checkout v0.11.0 # optional

2. Install Pytorch

Follow official instructions to install PyTorch of a supported version. Kaolin may be able to work with other PyTorch versions. See below for overriding PyTorch version check during install.

3. Optional Environment Variables

  • If trying Kaolin with an unsupported PyTorch version, set: export IGNORE_TORCH_VER=1

  • To install experimental features (like kaolin-dash3d), set: export KAOLIN_INSTALL_EXPERIMENTAL=1

  • If using heterogeneous GPU setup, set the architectures for which to compile the CUDA code, e.g.: export TORCH_CUDA_ARCH_LIST="7.0 7.5"

  • In some setups, there may be a conflict between cub available with cuda install > 11 and third_party/cub that kaolin includes as a submodule. If conflict occurs or cub is not found, set CUB_HOME to the cuda one, e.g. typically on Linux: export CUB_HOME=/usr/local/cuda-*/include/

4. Install Kaolin

$ python setup.py develop

Note

Kaolin can be installed without GPU, however, CPU support is limited and many CUDA-only functions will be missing.

Testing your installation

Run a quick test of your installation and version:

$ python -c "import kaolin; print(kaolin.__version__)"

Running tests

For an exhaustive check, install testing dependencies and run tests as follows:

$ pip install -r tools/ci_requirements.txt
$ pytest tests/python/

Note

These tests rely on CUDA operations and will fail if you installed on CPU only, where not all functionality is available.