How to Install and Uninstall python-pycuda-doc Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 16,2024

1. Install "python-pycuda-doc" package

Please follow the guidelines below to install python-pycuda-doc on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                python-pycuda-doc
            
                    
    
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2. Uninstall "python-pycuda-doc" package

Please follow the step by step instructions below to uninstall python-pycuda-doc on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                python-pycuda-doc
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the python-pycuda-doc package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: python-pycuda-doc
Priority: optional
Section: multiverse/doc
Installed-Size: 972
Maintainer: Ubuntu Developers
Original-Maintainer: Tomasz Rybak
Architecture: all
Source: pycuda
Version: 2016.1-1
Depends: libjs-sphinxdoc (>= 1.0), libjs-mathjax, fonts-mathjax
Recommends: nvidia-cuda-doc
Suggests: python-pycuda, python3-pycuda
Filename: pool/multiverse/p/pycuda/python-pycuda-doc_2016.1-1_all.deb
Size: 120940
MD5sum: c11a2d31fecbe0868a8e71b46809b508
SHA1: 8baffd3c51e55146a1d420a04e2f4dea57c0863c
SHA256: bfc099c7b4e1437d4d4d83206f67e339eb8bb75c1a645039158f17b8c2772062
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
Homepage: http://mathema.tician.de/software/pycuda
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu