Setup and Installation#

This guide walks you through installing and setting up the CUDA Python Core Libraries (CCCL).

Prerequisites#

Before installing cuda-cccl, ensure you have:

  • Python 3.9 or later

  • CUDA Toolkit 12.x or 13.x

  • Compatible NVIDIA GPU with Compute Capability 6.0 or higher

  • Operating Systems: Linux (tested on Ubuntu 20.04+) or Windows 10/11 (with WSL2 support)

Installation#

Install from PyPI#

The easiest way to install cuda-cccl is using pip:

pip install cuda-cccl[cu13]  # or cuda-cccl[cu12]

This will install cuda-cccl along with all required dependencies.

Install from Source#

For development or to access the latest features:

git clone https://github.com/NVIDIA/cccl.git
cd cccl/python/cuda_cccl
pip install -e .[test-cu13]  # or -e .[test-cu12]

Development Setup#

For contributing to cuda-cccl or advanced development:

# Clone the repository
git clone https://github.com/NVIDIA/cccl.git
cd cccl/python/cuda_cccl

# Install in development mode with test dependencies
pip install -e .[test-cu13]  # or -e .[test-cu12]

# Run tests to verify everything works
pytest tests/

Next Steps#

Now that you have cuda-cccl installed, check out: