Installation

Requirements

Supported GPUs

Numba supports all NVIDIA GPUs that are supported by the CUDA Toolkit it uses. Presently for CUDA 11 this ranges from Compute Capabilities 3.5 to 9.0, and for CUDA 12 this ranges from 5.0 to 12.1, depending on the exact installed version.

Supported CUDA Toolkits

Numba-CUDA aims to support all minor versions of the two most recent CUDA Toolkit releases. Presently 11 and 12 are supported; CUDA 11.2 is the minimum required, because older releases (11.0 and 11.1) have a version of NVVM based on a previous and incompatible LLVM version.

For further information about version compatibility between toolkit and driver versions, refer to CUDA Minor Version Compatibility.

Installation with a Python package manager

Conda users can install the CUDA Toolkit into a conda environment.

For CUDA 12:

$ conda install -c conda-forge numba-cuda "cuda-version>=12.0"

Alternatively, you can install all CUDA 12 dependencies from PyPI via pip:

$ pip install numba-cuda[cu12]

For CUDA 11, cudatoolkit is required:

$ conda install -c conda-forge numba-cuda "cuda-version>=11.2,<12.0"

or:

$ pip install numba-cuda[cu11]

If you are not using Conda/pip or if you want to use a different version of CUDA toolkit, CUDA Driver and Toolkit search paths describes how Numba searches for a CUDA toolkit.

Configuration

CUDA Bindings

Numba supports interacting with the CUDA Driver API via either the NVIDIA CUDA Python bindings or its own ctypes-based bindings. Functionality is equivalent between the two binding choices. The NVIDIA bindings are the default, and the ctypes bindings are now deprecated.

If you do not want to use the NVIDIA bindings, the (deprecated) ctypes bindings can be enabled by setting the environment variable NUMBA_CUDA_USE_NVIDIA_BINDING to "0".

CUDA Driver and Toolkit search paths

Default behavior

When using the NVIDIA bindings, searches for the CUDA driver and toolkit libraries use its built-in path-finding logic.

Ctypes bindings (deprecated) behavior

When using the ctypes bindings, Numba searches for a CUDA toolkit installation in the following order:

  1. Conda-installed CUDA Toolkit packages

  2. Pip-installed CUDA Toolkit packages

  3. The environment variable CUDA_HOME, which points to the directory of the installed CUDA toolkit (i.e. /home/user/cuda-12)

  4. System-wide installation at exactly /usr/local/cuda on Linux platforms. Versioned installation paths (i.e. /usr/local/cuda-12.0) are intentionally ignored. Users can use CUDA_HOME to select specific versions.

In addition to the CUDA toolkit libraries, which can be installed by conda into an environment or installed system-wide by the CUDA SDK installer, the CUDA target in Numba also requires an up-to-date NVIDIA driver. Updated NVIDIA drivers are also installed by the CUDA SDK installer, so there is no need to do both. If the libcuda library is in a non-standard location, users can set environment variable NUMBA_CUDA_DRIVER to the file path (not the directory path) of the shared library file.