How to Install and Uninstall python3-faiss Package on Kali Linux

Last updated: November 22,2024

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

1. Install "python3-faiss" package

This tutorial shows how to install python3-faiss on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                python3-faiss
            
                    
    
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2. Uninstall "python3-faiss" package

This is a short guide on how to uninstall python3-faiss on Kali Linux:


    
        
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                sudo apt remove
            
                        
                python3-faiss
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the python3-faiss package on Kali Linux

Package: python3-faiss
Source: faiss
Version: 1.7.4-3
Installed-Size: 8568
Maintainer: Debian Deep Learning Team
Architecture: amd64
Depends: python3 (<< 3.12), python3 (>= 3.11~), python3-numpy (>= 1:1.22.0), python3-numpy-abi9, python3:any, libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.4), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 12)
Size: 1722396
SHA256: 8f7a78d50b1ba7468a6bdc1278af1c15093cb7c1844c3ebe398fce14dc3f36db
SHA1: 30fb6ad3ba014fe67ab4620e19926364a9fa1ae1
MD5sum: a12a9dd646969dbebcb4a940980d436d
Description: Python 3 module for efficient similarity search and clustering of dense vectors
Faiss is a library for efficient similarity search and clustering of dense
vectors. It contains algorithms that search in sets of vectors of any size, up
to ones that possibly do not fit in RAM. It also contains supporting code for
evaluation and parameter tuning. Faiss is written in C++ with complete wrappers
for Python/numpy. Some of the most useful algorithms are implemented on the
GPU. It is developed by Facebook AI Research.
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This package contains the CPU-only version of the Python interface.
Description-md5:
Homepage: https://github.com/facebookresearch/faiss
Section: science
Priority: optional
Filename: pool/main/f/faiss/python3-faiss_1.7.4-3_amd64.deb