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 functionality
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.compute: 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-mlir: An evolution of Numba CUDA that improves upon its technical foundation and performance to provide the future of CUDA Python JIT compilation. It currently supports developing CUDA SIMT kernels in Python, providing Python bindings for accelerated device libraries, and serving as a compiler for user-defined functions in accelerated libraries.
numba.cuda: A Python DSL that exposes CUDA SIMT programming model and compiles a restricted subset of Python code into CUDA kernels and device functions
cuda.tile: A new Python DSL that exposes CUDA Tile programming model and allows users to write NumPy-like code in CUDA kernels
nvmath-python: Pythonic access to NVIDIA CPU & GPU Math Libraries, with host, device, and distributed APIs. It also provides low-level Python bindings to host C APIs (nvmath.bindings).
nvshmem4py: Pythonic interface to the NVSHMEM library, enabling Python applications to leverage NVSHMEM’s high-performance PGAS (Partitioned Global Address Space) programming model for GPU-accelerated computing
Nsight Python: Python kernel profiling interface that automates performance analysis across multiple kernel configurations using NVIDIA Nsight Tools
CUPTI Python: Python APIs for creation of profiling tools that target CUDA Python applications via the CUDA Profiling Tools Interface (CUPTI)
Accelerated Computing Hub: Open-source learning materials related to GPU computing. You will find user guides, tutorials, and other works freely available for all learners interested in GPU computing.
CUDA Python is currently undergoing an overhaul to improve existing and introduce new components. All of the previously available functionality 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 as a metapackage
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.
Subpackage: cuda.core
The cuda.core package offers idiomatic, pythonic access to CUDA Runtime and other functionality.
The goals are to
Provide idiomatic (“pythonic”) access to CUDA Driver, Runtime, and JIT compiler toolchain
Focus on developer productivity by ensuring end-to-end CUDA development can be performed quickly and entirely in Python
Avoid homegrown Python abstractions for CUDA for new Python GPU libraries starting from scratch
Ease developer burden of maintaining and catching up with latest CUDA features
Flatten the learning curve for current and future generations of CUDA developers
Subpackage: cuda.bindings
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
nvFatbin
cuFile
NVML
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