pycuda 2018.1.1-3build2 source package in Ubuntu

Changelog

pycuda (2018.1.1-3build2) disco; urgency=medium

  * No-change rebuild against CUDA 10.1

 -- Graham Inggs <email address hidden>  Wed, 27 Mar 2019 17:52:37 +0000

Upload details

Uploaded by:
Graham Inggs on 2019-03-27
Uploaded to:
Disco
Original maintainer:
Debian NVIDIA Maintainers
Architectures:
amd64 all
Section:
python
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Eoan release on 2019-04-18 multiverse python
Disco release on 2019-03-27 multiverse python

Builds

Disco: [FULLYBUILT] amd64

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File Size SHA-256 Checksum
pycuda_2018.1.1.orig.tar.xz 180.4 KiB 473359d8f6e38849c331418ed986c2519cf2c11e140a05b44236ff7306c6bf25
pycuda_2018.1.1-3build2.debian.tar.xz 10.7 KiB 914721d92a41e54b4e97ce62c991a7bbf3fe75b8add344fee12bbb8f905aaf5d
pycuda_2018.1.1-3build2.dsc 2.7 KiB d0d54c91f895d5d69365c3e77ff66653269e3c3c4e89f0ab857469738c2c9664

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Binary packages built by this source

python-pycuda: Python 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.

python-pycuda-dbg: Python module to access Nvidia‘s CUDA API (debug extensions)

 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 debug extensions build for the Python debug interpreter.

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-dbg: Python 3 module to access Nvidia‘s CUDA API (debug extensions)

 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 debug extensions for the Python 3 debug interpreter.