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pycuda package

pycuda 2024.1~dfsg-1build2 source package in Ubuntu

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pycuda (2024.1~dfsg-1build2) noble; urgency=medium

  * No-change rebuild for CVE-2024-3094

 -- William Grant <email address hidden>  Mon, 01 Apr 2024 18:18:08 +1100

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Uploaded by:
William Grant
Uploaded to:
Noble
Original maintainer:
Ubuntu Developers
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Oracular release multiverse python
Noble release multiverse python

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File Size SHA-256 Checksum
pycuda_2024.1~dfsg.orig.tar.xz 174.9 KiB 799ab587dea03ee4562f0dece1886a43bd36e897a21cc2b233357d4ce2475b3f
pycuda_2024.1~dfsg-1build2.debian.tar.xz 23.1 KiB 4acdc39d75a36d8edebebef8597bb4b23ef2b3142e0a8828ef861ddfd961c165
pycuda_2024.1~dfsg-1build2.dsc 2.6 KiB fc71390334fee73ee95074c07969be1fb692b4cee508afa00534de91282ae534

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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