How to Install and Uninstall python3-pycuda-dbg Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 19,2024

1. Install "python3-pycuda-dbg" package

This is a short guide on how to install python3-pycuda-dbg on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
            $
        

        
            
                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                python3-pycuda-dbg
            
                    
    
    Copied

2. Uninstall "python3-pycuda-dbg" package

This tutorial shows how to uninstall python3-pycuda-dbg on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
            $
        

        
            
                sudo apt remove
            
                        
                python3-pycuda-dbg
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the python3-pycuda-dbg package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: python3-pycuda-dbg
Priority: extra
Section: multiverse/debug
Installed-Size: 16123
Maintainer: Ubuntu Developers
Original-Maintainer: Tomasz Rybak
Architecture: amd64
Source: pycuda
Version: 2016.1-1
Depends: python3-pycuda (= 2016.1-1), python3-dbg (<< 3.6), 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), python3-numpy (>= 1:1.10.0~b1), python3-numpy-abi9, python3-dbg (>= 3.5~)
Filename: pool/multiverse/p/pycuda/python3-pycuda-dbg_2016.1-1_amd64.deb
Size: 4654934
MD5sum: b6a3cb2ff7cfffdf06ab6a9b93b6aed7
SHA1: 7e08e32bbe270d7828f35ab2e5e057ff008708a9
SHA256: a0440dc0fba01b22b59ad4257a0473019685eba9aec20836c605a622184d177f
Description-en: 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.
Description-md5: 2408be5275171c7291f7d0f275b7d393
Homepage: http://mathema.tician.de/software/pycuda
Build-Ids: 3af28c9d64616a46ab0bf86bebc9087df6ea7379 3cdc3de417e21940f53aa7eda2660fc4f31f6de4
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu