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pycuda package

pycuda 2021.1~dfsg-2build2 source package in Ubuntu

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pycuda (2021.1~dfsg-2build2) jammy; urgency=medium

  * No-change rebuild with Python 3.10 only

 -- Graham Inggs <email address hidden>  Wed, 16 Mar 2022 22:54:33 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Jammy
Original maintainer:
Debian NVIDIA Maintainers
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Jammy release multiverse python

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pycuda_2021.1~dfsg.orig.tar.xz 183.0 KiB af97f286f38bf6f3633fd3ec14a795d41cc18e79a792137dd0ee858631c49884
pycuda_2021.1~dfsg-2build2.debian.tar.xz 31.2 KiB 41e8709afce9f7763b8b6a67f4826c5b7012a32d51091e3347f6ddb9a1752387
pycuda_2021.1~dfsg-2build2.dsc 2.5 KiB 8788fb6c7a873b5d5fee015600ddaacd874574eafc312ad132bb30eb0d14166e

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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