CUDA Python is the home for accessing NVIDIA’s CUDA platform from Python. It consists of multiple components:

• cuda.core: Pythonic access to CUDA Runtime and other core functionalities
• cuda.bindings: Low-level Python bindings to CUDA C APIs
• cuda.pathfinder: Utilities for locating CUDA components installed in the user's Python environment
• cuda.cccl.cooperative: A Python module providing CCCL's reusable block-wide and warp-wide device primitives for use within Numba CUDA kernels
• cuda.cccl.parallel: A Python module for easy access to CCCL's highly efficient and customizable parallel algorithms, like sort, scan, reduce, transform, etc. that are callable on the host
• numba.cuda: Numba's target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
• nvmath-python: Pythonic access to NVIDIA CPU & GPU Math Libraries, with both host and device (nvmath.device) APIs. It also provides low-level Python bindings to host C APIs (nvmath.bindings).

CUDA Python is currently undergoing an overhaul to improve existing and introduce new components. All of the previously available functionalities from the cuda-python package will continue to be available, please refer to the cuda.bindings documentation for installation guide and further detail.

cuda-python is being restructured to become a metapackage that contains a collection of subpackages. Each subpackage is versioned independently, allowing installation of each component as needed.

The cuda.core package offers idiomatic, Pythonic access to CUDA Runtime and other functionalities.

The goals are to

1. Provide idiomatic ("Pythonic") access to CUDA Driver, Runtime, and JIT compiler toolchain
2. Focus on developer productivity by ensuring end-to-end CUDA development can be performed quickly and entirely in Python
3. Avoid homegrown Python abstractions for CUDA for new Python GPU libraries starting from scratch
4. Ease developer burden of maintaining and catching up with latest CUDA features
5. Flatten the learning curve for current and future generations of CUDA developers

The cuda.bindings package is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python.

The list of available interfaces is:

• CUDA Driver
• CUDA Runtime
• NVRTC
• nvJitLink
• NVVM
• cuFile