How to Install and Uninstall python-pycuda-doc Package on Ubuntu 21.10 (Impish Indri)
Last updated: November 22,2024
1. Install "python-pycuda-doc" package
Please follow the guidelines below to install python-pycuda-doc on Ubuntu 21.10 (Impish Indri)
$
sudo apt update
Copied
$
sudo apt install
python-pycuda-doc
Copied
2. Uninstall "python-pycuda-doc" package
Please follow the step by step instructions below to uninstall python-pycuda-doc on Ubuntu 21.10 (Impish Indri):
$
sudo apt remove
python-pycuda-doc
Copied
$
sudo apt autoclean && sudo apt autoremove
Copied
3. Information about the python-pycuda-doc package on Ubuntu 21.10 (Impish Indri)
Package: python-pycuda-doc
Architecture: all
Version: 2020.1~dfsg1-1build1
Multi-Arch: foreign
Priority: optional
Section: multiverse/doc
Source: pycuda
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian NVIDIA Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1134
Depends: fonts-mathjax, libjs-mathjax, libjs-sphinxdoc (>= 2.4.3-5~)
Recommends: nvidia-cuda-doc, python-mako-doc, python-numpy-doc, python3-doc
Suggests: python3-pycuda
Filename: pool/multiverse/p/pycuda/python-pycuda-doc_2020.1~dfsg1-1build1_all.deb
Size: 125736
MD5sum: 15dd889d5c91bc9dc291505272a59ab7
SHA1: 732a8163cfc4fe53d7b0c7f3b658a7b2a7ab8469
SHA256: 815e02dafd1b560dd7686be68cc34ee72e051442886dc29af7be51fa1bba2f76
SHA512: b06161a5fdd1486d70ce2afded3d96364e12fb4a80065a3eaedb80901456ebb73b53832ee32647437b359671b1ae3efd75f97b63ff8cff5aa0016e4dd0a4458d
Homepage: http://mathema.tician.de/software/pycuda
Description-en: 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.
Description-md5: 4b4f2b1e8b32879eefe98c99f3a598ba
Architecture: all
Version: 2020.1~dfsg1-1build1
Multi-Arch: foreign
Priority: optional
Section: multiverse/doc
Source: pycuda
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian NVIDIA Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1134
Depends: fonts-mathjax, libjs-mathjax, libjs-sphinxdoc (>= 2.4.3-5~)
Recommends: nvidia-cuda-doc, python-mako-doc, python-numpy-doc, python3-doc
Suggests: python3-pycuda
Filename: pool/multiverse/p/pycuda/python-pycuda-doc_2020.1~dfsg1-1build1_all.deb
Size: 125736
MD5sum: 15dd889d5c91bc9dc291505272a59ab7
SHA1: 732a8163cfc4fe53d7b0c7f3b658a7b2a7ab8469
SHA256: 815e02dafd1b560dd7686be68cc34ee72e051442886dc29af7be51fa1bba2f76
SHA512: b06161a5fdd1486d70ce2afded3d96364e12fb4a80065a3eaedb80901456ebb73b53832ee32647437b359671b1ae3efd75f97b63ff8cff5aa0016e4dd0a4458d
Homepage: http://mathema.tician.de/software/pycuda
Description-en: 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.
Description-md5: 4b4f2b1e8b32879eefe98c99f3a598ba