How to Install and Uninstall libxnnpack0 Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 17,2024

1. Install "libxnnpack0" package

This guide covers the steps necessary to install libxnnpack0 on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libxnnpack0
            
                    
    
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2. Uninstall "libxnnpack0" package

Please follow the instructions below to uninstall libxnnpack0 on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                libxnnpack0
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libxnnpack0 package on Ubuntu 21.10 (Impish Indri)

Package: libxnnpack0
Architecture: amd64
Version: 0.0~git20201031.beca652+really.git20200323.1b35463-2
Priority: optional
Section: universe/math
Source: xnnpack
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Deep Learning Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1275
Depends: libc6 (>= 2.29), libcpuinfo0 (>= 0.0~git20200422.a1e0b95-2~), libpthreadpool0 (>= 0.0~git20200423.6acde9c)
Filename: pool/universe/x/xnnpack/libxnnpack0_0.0~git20201031.beca652+really.git20200323.1b35463-2_amd64.deb
Size: 379896
MD5sum: c62031ceb18d0c46aad2035d9c3f1315
SHA1: e5b8adef6b9309e24e38c34c528767bd23e6a469
SHA256: 117af70006ef98ea0e56bf622e60785eecadacc3af26420a3f50f862327509b2
SHA512: 8fccfa1b049b3fcd3f36c41215489a800db6639ea8bdc6d8a196863748a862cb0dd7375bb71d0491754e1dd913e2dfdeca77cca7f1e5700571c481b68f03eeaf
Homepage: https://github.com/google/XNNPACK
Description-en: High-efficiency floating-point neural network inference operators (libs)
XNNPACK is a highly optimized library of floating-point neural network
inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not
intended for direct use by deep learning practitioners and researchers; instead
it provides low-level performance primitives for accelerating high-level
machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch,
and MediaPipe.
.
This package contains the shared object.
Description-md5: dc9d241c9273e8b3ff6857bfb088465d