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pycuda package

pycuda 2022.2.2~dfsg-2build1 source package in Ubuntu

Changelog

pycuda (2022.2.2~dfsg-2build1) lunar; urgency=medium

  * Rebuild against latest icu

 -- Jeremy Bicha <email address hidden>  Mon, 06 Feb 2023 07:59:59 -0500

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Uploaded by:
Jeremy Bícha
Uploaded to:
Lunar
Original maintainer:
Debian NVIDIA Maintainers
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Mantic release multiverse python
Lunar release multiverse python

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pycuda_2022.2.2~dfsg.orig.tar.xz 184.7 KiB 64b624efa4dd721ed7d602f396486ccf5bf6a39005021f2e1ef0f09397018fce
pycuda_2022.2.2~dfsg-2build1.debian.tar.xz 22.9 KiB edc1c3eb3e4bf5b0a9c24ebb79ff482e7d1a77ae91770a14fa671c341c0ac6df
pycuda_2022.2.2~dfsg-2build1.dsc 2.5 KiB 7e6618a916c087d0dfc7db6bf18ce61abb5bd2d8c5704f53ce7a4cf832746c71

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Binary packages built by this source

python-pycuda-doc: module to access Nvidia‘s CUDA computation API (documentation)

 PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
 Several wrappers of the CUDA API already exist–so what’s so special about
 PyCUDA?
  * Object cleanup tied to lifetime of objects. This idiom, often called
    RAII in C++, makes it much easier to write correct, leak- and crash-free
    code. PyCUDA knows about dependencies, too, so (for example) it won’t
    detach from a context before all memory allocated in it is also freed.
  * Convenience. Abstractions like pycuda.driver.SourceModule and
    pycuda.gpuarray.GPUArray make CUDA programming even more convenient than
    with Nvidia’s C-based runtime.
  * Completeness. PyCUDA puts the full power of CUDA’s driver API at your
    disposal, if you wish.
  * Automatic Error Checking. All CUDA errors are automatically translated
    into Python exceptions.
  * Speed. PyCUDA’s base layer is written in C++, so all the niceties
    above are virtually free.
  * Helpful Documentation.
 .
 This package contains HTML documentation and example scripts.

python3-pycuda: Python 3 module to access Nvidia‘s CUDA parallel computation API

 PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
 Several wrappers of the CUDA API already exist–so what’s so special about
 PyCUDA?
  * Object cleanup tied to lifetime of objects. This idiom, often called
    RAII in C++, makes it much easier to write correct, leak- and crash-free
    code. PyCUDA knows about dependencies, too, so (for example) it won’t
    detach from a context before all memory allocated in it is also freed.
  * Convenience. Abstractions like pycuda.driver.SourceModule and
    pycuda.gpuarray.GPUArray make CUDA programming even more convenient than
    with Nvidia’s C-based runtime.
  * Completeness. PyCUDA puts the full power of CUDA’s driver API at your
    disposal, if you wish.
  * Automatic Error Checking. All CUDA errors are automatically translated
    into Python exceptions.
  * Speed. PyCUDA’s base layer is written in C++, so all the niceties
    above are virtually free.
  * Helpful Documentation.
 .
 This package contains Python 3 modules.

python3-pycuda-dbgsym: debug symbols for python3-pycuda