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

Last updated: November 26,2024

1. Install "python-pycuda" package

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


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

Please follow the guidance below to uninstall python-pycuda on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                python-pycuda
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the python-pycuda package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: python-pycuda
Priority: optional
Section: multiverse/python
Installed-Size: 1867
Maintainer: Ubuntu Developers
Original-Maintainer: Tomasz Rybak
Architecture: amd64
Source: pycuda
Version: 2016.1-1
Replaces: python-pycuda-headers
Depends: libboost-python1.58.0, libboost-thread1.58.0, libc6 (>= 2.14), libcuda-5.5-1, libcurand7.5 (>= 4.0), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), python-appdirs (>= 1.4.0), python-decorator (>= 3.2.0), python-pytools (>= 2011.5), python:any (>= 2.7.5-5~), nvidia-cuda-toolkit
Recommends: python-pycuda-doc, python-mako
Suggests: python-pytest, python-opengl, python-matplotlib, python-pycuda-dbg
Filename: pool/multiverse/p/pycuda/python-pycuda_2016.1-1_amd64.deb
Size: 306652
MD5sum: 0d02a0d38cb8e00c5e9811dd3fde9d21
SHA1: b4a73c073681192b27de5204d40a99a56791bb5a
SHA256: b1ba37f8a1bcb6ce3367eae8f4de5c61046fab21795f35988d35bad4a52a403b
Description-en: 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.
Description-md5: 999e10331c8e1f852d56bf92b2010d4b
Homepage: http://mathema.tician.de/software/pycuda
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu