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CUDA Python 11.4 documentation
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CUDA Python 11.4 documentation
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Contents:

  * Overview
  * Installation
  * Quickstart
  * Code of Conduct
  * Contributing
  * Release Notes[ ]
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      + 11.4.0
  * CUDA Python API Reference

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OverviewP

NVIDIA's CUDA Python provides Cython bindings and Python wrappers for
the driver and runtime API for existing toolkits and libraries to
simplify GPU-based accelerated processing. Python is one of the most
popular programming languages for science, engineering, data
analytics, and deep learning applications. However, as an interpreted
language, it has limitations for high-performance computing
applications. The goal of CUDA Python is to unify the Python
ecosystem with a single set of interfaces that provide full coverage
of and access to the CUDA host APIs from Python.

Why CUDA Python?P

CUDA Python provides uniform APIs and bindings for inclusion into
existing toolkits and libraries to simplify GPU-based parallel
processing for HPC, data science, and AI.

Numba, a Python compiler from Anaconda that can compile Python code
for execution on CUDA-capable GPUs, provides Python developers with
an easy entry into GPU-accelerated computing and a path for using
increasingly sophisticated CUDA code with a minimum of new syntax and
jargon. With CUDA Python and Numba, you get the best of both worlds:
rapid iterative development with Python and the speed of a compiled
language targeting both CPUs and NVIDIA GPUs.

CuPy is a NumPy/SciPy compatible Array library, from Preferred
Networks, for GPU-accelerated computing with Python. CUDA Python
simplifies the CuPy build and allows for a faster and smaller memory
footprint when importing the CuPy Python module. In the future, when
more CUDA Toolkit libraries are supported, CuPy will have a lighter
maintenance overhead and have fewer wheels to release. Users benefit
from a faster CUDA runtime!

EcosystemP

Our goal is to help unify the Python CUDA ecosystem with a single
standard set of interfaces, providing full coverage of, and access
to, the CUDA host APIs from Python. We want to provide a foundation
for the ecosystem to build on top of in unison to allow composing
different accelerated libraries together to solve the problems at
hand. We also want to lower the barrier to entry for Python
developers to utilize NVIDIA GPUs.

 
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Contents

  * Overview
      + Why CUDA Python?
      + Ecosystem