cuda.cccl provides a Pythonic interface to the CUDA Core Compute Libraries. It provides the following modules:

• cuda.compute - Device-level parallel algorithms (reduce, scan, sort, etc.) and iterators
• cuda.coop._experimental - Block and warp-level cooperative primitives for custom CUDA kernels

Install from PyPI:

pip install cuda-cccl[cu13]  # For CUDA 13.x (pip-installed cuda-toolkit)
pip install cuda-cccl[cu12]  # For CUDA 12.x (pip-installed cuda-toolkit)

If you already have a CUDA toolkit on your system and do not want pip to install it, use the sysctk variants:

pip install cuda-cccl[sysctk13]  # For CUDA 13.x (system CUDA toolkit)
pip install cuda-cccl[sysctk12]  # For CUDA 12.x (system CUDA toolkit)

For a minimal install without Numba (useful when supplying pre-compiled operators):

pip install cuda-cccl[minimal-cu13]      # pip-installed cuda-toolkit
pip install cuda-cccl[minimal-sysctk13]  # system CUDA toolkit

Install from conda-forge:

conda install -c conda-forge cccl-python

Requirements: Python 3.10+, CUDA Toolkit 12.x or 13.x, NVIDIA GPU with Compute Capability 6.0+

For complete documentation, examples, and API reference, visit:

• Full Documentation: nvidia.github.io/cccl/python
• Repository: github.com/NVIDIA/cccl
• Examples: github.com/NVIDIA/cccl/tree/main/python/cuda_cccl/tests/compute/examples and github.com/NVIDIA/cccl/tree/main/python/cuda_cccl/tests/coop/_experimental/examples