pycuda 2018.1.1-1ubuntu1 source package in Ubuntu

Changelog

pycuda (2018.1.1-1ubuntu1) disco; urgency=low

  * Merge from Debian unstable.  Remaining changes:
    - Fix ftbfs due to boost-python soname change.

pycuda (2018.1.1-1) unstable; urgency=medium

  [ Andreas Beckmann ]
  * Put package under maintenance by the Debian NVIDIA Maintainers team, move
    Tomasz to Uploaders.
  * Switch Vcs-* URLs to salsa.debian.org.

  [ Tomasz Rybak ]
  * New upstream release (Closes: #903826).
  * Add Rules-Requires-Root to d/control.
  * Update d/copyright links to use https protocol.
  * Add disclaimer to d/copyright describing why PyCUDA is in contrib.
  * Reorder d/control putting Python 3 packages first.
  * Remove unnecessary X-Python{,3}-Version fields.
  * Update Standards-Version to 4.2.1; no changes necessary.
  * Set compatibility level to 11
    * Point python-pycuda-doc.doc-base to main package's doc directory.

 -- Gianfranco Costamagna <email address hidden>  Fri, 09 Nov 2018 15:13:18 +0100

Upload details

Uploaded by:
Gianfranco Costamagna on 2018-11-09
Uploaded to:
Disco
Original maintainer:
Debian NVIDIA Maintainers
Architectures:
amd64 all
Section:
python
Urgency:
Medium Urgency

See full publishing history Publishing

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Disco release on 2018-11-09 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_2018.1.1-1ubuntu1.debian.tar.xz 10.6 KiB 9ae9f03f3bceabb2e035290a9f2bdf849704387cc1702f07a28d518bdf09f258
pycuda_2018.1.1-1ubuntu1.dsc 2.7 KiB 027a117870d0aa0e199377c584fbc191556339fbc4b97ef8faa360ecaa933246

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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.