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


  • Linux, macOS or Windows

  • Python >= 3.6 (3.6 and 3.7 recommended for Windows)

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


  • torch >= 1.5, <= 1.7.1

  • cython == 0.29.20

  • scipy >= 1.2.0

  • Pillow >= 8.0.0

  • usd-core >= 20.11 (optional, required for USD related features such as visualization and importer / exporter)

Installation from source


If you just want to try Kaolin, we recommend using a virtual environment, for instance with Anaconda:

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

We recommend following instructions from for installing PyTorch, and for installing cython, however Kaolin installation will attempt to automatically install the latest compatible version if none is installed (may fail on some systems). Kaolin may also function with some incompatible PyTorch versions; to override the PyTorch version check, set environment variable export IGNORE_TORCH_VER=1 before installing.

To install the library. You must first clone the repository:

$ git clone --recursive
$ cd kaolin

If instead of the latest version you want a specific release like 0.9.0, 0.9.1 or 0.1 you can then select the tag, example:

$ git checkout v0.9.1

To enable installation of experimental features, set environment variable export KAOLIN_INSTALL_EXPERIMENTAL=1. To install, run:

$ python develop


If you are using heterogeneous GPUs setup set the architectures for which you want to compile the cuda code using the TORCH_CUDA_ARCH_LIST environment variable.


$ export TORCH_CUDA_ARCH_LIST="7.0 7.5"


Kaolin can be installed without GPU, however, CPU support is limited to some ops.

Testing your installation

A quick test is to see if you can properly import kaolin and print the current version by running the following:

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

Running tests

A more exhaustive test is to execute all the official tests.

First, pytest dependencies are necessary to run those tests, to install those run:

$ pip install -r tools/ci_requirements.txt

Then run the tests as following:

$ pytest tests/python/


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