How to Install and Uninstall libfaiss-dev Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 22,2024

1. Install "libfaiss-dev" package

Please follow the guidance below to install libfaiss-dev on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libfaiss-dev
            
                    
    
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2. Uninstall "libfaiss-dev" package

Here is a brief guide to show you how to uninstall libfaiss-dev on Ubuntu 21.04 (Hirsute Hippo):


    
        
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                sudo apt remove
            
                        
                libfaiss-dev
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libfaiss-dev package on Ubuntu 21.04 (Hirsute Hippo)

Package: libfaiss-dev
Architecture: amd64
Version: 1.7.0-1
Priority: optional
Section: universe/science
Source: faiss
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Deep Learning Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 5185
Recommends: libopenblas-dev | libblis-dev | intel-mkl | libblas-dev
Filename: pool/universe/f/faiss/libfaiss-dev_1.7.0-1_amd64.deb
Size: 691356
MD5sum: fe53c4dcdabacc72daa12fc594fa5e24
SHA1: 1b6187a56ed565387ba7122a128c1ec9a7b8752f
SHA256: 8e360c2d7448c887ef99a09273762de4121bdd5ff550222f4f50b99e27269bfa
SHA512: ba62affa415c69a71388be8f73f6bfae1a66c619a350699f17f6a26da82ec42da44149765279c3ad3e6fc4a7d7a050dcf9eeabde714c2c3ca683af855a1b0660
Homepage: https://github.com/facebookresearch/faiss
Description-en: 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.
.
This package contains the CPU-only version of the development files.
Description-md5: 97a446d4f7c6eb17e90eec293f6aba51