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pycuda package

pycuda 2022.2~dfsg-1 source package in Ubuntu

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pycuda (2022.2~dfsg-1) unstable; urgency=medium

  * New upstream release.
  * Drop ancient d/NEWS.

 -- Andreas Beckmann <email address hidden>  Tue, 22 Nov 2022 22:51:06 +0100

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

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pycuda_2022.2~dfsg-1.dsc 2.8 KiB 4527b0c58c72a54dc7fe42184433040e241b16dd528da7ab1e2b5ccf2f13c6c1
pycuda_2022.2~dfsg.orig.tar.xz 184.6 KiB 3fb6946ccf5ed0ff7ccc59da7f131cfa5d62ac091b5b5ff70cd1630fb53efe5f
pycuda_2022.2~dfsg-1.debian.tar.xz 24.4 KiB e63c0aaaa8375186907272d53489ccad59403e7b0a4f0a8855480fa80d482229

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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