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Installation

Runtime Requirements

cuda.bindings supports the same platforms as CUDA. Runtime dependencies are:

  • Linux (x86-64, arm64) and Windows (x86-64)
  • Python 3.9 - 3.13
  • Driver: Linux (580.65.06 or later) Windows (580.88 or later)
  • Optionally, NVRTC, nvJitLink, NVVM, and cuFile from CUDA Toolkit 13.x
The optional CUDA Toolkit components are now installed via the `cuda-toolkit` metapackage from PyPI for improved dependency resolution. Components can also be installed via Conda, OS-specific package managers, or local installers (as described in the CUDA Toolkit [Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) and [Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) Installation Guides).

Starting from v12.8.0, cuda-python becomes a meta package which currently depends only on cuda-bindings; in the future more sub-packages will be added to cuda-python. In the instructions below, we still use cuda-python as example to serve existing users, but everything is applicable to cuda-bindings as well.

Installing from PyPI

$ pip install -U cuda-python

Install all optional dependencies with:

pip install -U cuda-python[all]

Where the optional dependencies include:

  • nvidia-cuda-nvrtc (NVRTC runtime compilation library)
  • nvidia-nvjitlink (nvJitLink library)
  • nvidia-nvvm (NVVM library)
  • nvidia-cufile (cuFile library, Linux only)

These are now installed through the cuda-toolkit metapackage for improved dependency resolution.

Installing from Conda

$ conda install -c conda-forge cuda-python
When using conda, the `cuda-version` metapackage can be used to control the versions of CUDA Toolkit components that are installed to the conda environment.

For example:

$ conda install -c conda-forge cuda-python cuda-version=13

Installing from Source

Requirements

  • CUDA Toolkit headers1
  • CUDA Runtime static library2

Source builds require that the provided CUDA headers are of the same major.minor version as the cuda.bindings you're trying to build. Despite this requirement, note that the minor version compatibility is still maintained. Use the CUDA_HOME (or CUDA_PATH) environment variable to specify the location of your headers. For example, if your headers are located in /usr/local/cuda/include, then you should set CUDA_HOME with:

$ export CUDA_HOME=/usr/local/cuda

See Environment Variables for a description of other build-time environment variables.

Only `cydriver`, `cyruntime` and `cynvrtc` are impacted by the header requirement.

Editable Install

You can use

$ pip install -v -e .

to install the module as editable in your current Python environment (e.g. for testing of porting other libraries to use the binding).

Footnotes

  1. User projects that cimport CUDA symbols in Cython must also use CUDA Toolkit (CTK) types as provided by the cuda.bindings major.minor version. This results in CTK headers becoming a transitive dependency of downstream projects through CUDA Python. ↩

  2. The CUDA Runtime static library (libcudart_static.a on Linux, cudart_static.lib on Windows) is part of the CUDA Toolkit. If using conda packages, it is contained in the cuda-cudart-static package. ↩