How to Install and Uninstall libgloo-cuda-dev Package on Kali Linux

Last updated: December 28,2024

Deprecated! Installation of this package may no longer be supported.

1. Install "libgloo-cuda-dev" package

Please follow the steps below to install libgloo-cuda-dev on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                libgloo-cuda-dev
            
                    
    
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2. Uninstall "libgloo-cuda-dev" package

Please follow the guidance below to uninstall libgloo-cuda-dev on Kali Linux:


    
        
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                sudo apt remove
            
                        
                libgloo-cuda-dev
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the libgloo-cuda-dev package on Kali Linux

Package: libgloo-cuda-dev
Source: gloo-cuda
Version: 0.0~git20230117.1090929-2
Installed-Size: 352
Maintainer: Debian Deep Learning Team
Architecture: amd64
Replaces: libgloo-dev
Depends: libgloo-cuda-0 (= 0.0~git20230117.1090929-2), libhiredis-dev, libibverbs-dev
Conflicts: libgloo-dev
Size: 57308
SHA256: ea27d1d05f74079cabdb133e64946dbed427992d393f6979563a7542ffb0ac64
SHA1: b9d0c8e306275b890636ab7cb390fcda7fd5dade
MD5sum: a6a3c19edb87dad00b00906d006835ac
Description: Collective communications library (development files)
Gloo is a collective communications library. It comes with a number of
collective algorithms useful for machine learning applications. These
include a barrier, broadcast, and allreduce.
.
Transport of data between participating machines is abstracted so that
IP can be used at all times, or InifiniBand (or RoCE) when available.
In the latter case, if the InfiniBand transport is used, GPUDirect can
be used to accelerate cross machine GPU-to-GPU memory transfers.
.
Where applicable, algorithms have an implementation that works with system
memory buffers, and one that works with NVIDIA GPU memory buffers. In the
latter case, it is not necessary to copy memory between host and device;
this is taken care of by the algorithm implementations.
.
This package ships the development files.
Description-md5:
Homepage: https://github.com/facebookincubator/gloo
Section: contrib/libdevel
Priority: optional
Filename: pool/contrib/g/gloo-cuda/libgloo-cuda-dev_0.0~git20230117.1090929-2_amd64.deb