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:
parallel: Device-Level Parallel Algorithms - Device-level parallel algorithms
cooperative: Cooperative Algorithms - Block and warp-level cooperative primitives