How to Install and Uninstall libfaiss-dev Package on Linux Mint 21.3 (Virginia)
Last updated: November 07,2024
1. Install "libfaiss-dev" package
Please follow the steps below to install libfaiss-dev on Linux Mint 21.3 (Virginia)
$
sudo apt update
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$
sudo apt install
libfaiss-dev
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2. Uninstall "libfaiss-dev" package
This guide covers the steps necessary to uninstall libfaiss-dev on Linux Mint 21.3 (Virginia):
$
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 Linux Mint 21.3 (Virginia)
Package: libfaiss-dev
Architecture: amd64
Version: 1.7.2-5
Multi-Arch: same
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: 6078
Depends: libblas-dev | libblas.so, liblapack-dev | liblapack.so
Filename: pool/universe/f/faiss/libfaiss-dev_1.7.2-5_amd64.deb
Size: 949358
MD5sum: c677d81e3cb4f66af7dcb215c997f0d7
SHA1: ced390a80ee860e80f984b2c8b31a8fedba37ad4
SHA256: 19fee6b5ee48896e3381dee2cd43d5ff0cfc637a000fc2fb24548eca807c275a
SHA512: 82c8e5d01472538ae0a694979cdc1b455e88475eb1f304150d8d6e02d511db2ad517fafe010c555c6d57bec9b3475159427919304fa8a558bc80de7668dda171
Homepage: https://github.com/facebookresearch/faiss
Description: efficient similarity search and clustering of dense vectors
Description-md5: 97a446d4f7c6eb17e90eec293f6aba51
Architecture: amd64
Version: 1.7.2-5
Multi-Arch: same
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: 6078
Depends: libblas-dev | libblas.so, liblapack-dev | liblapack.so
Filename: pool/universe/f/faiss/libfaiss-dev_1.7.2-5_amd64.deb
Size: 949358
MD5sum: c677d81e3cb4f66af7dcb215c997f0d7
SHA1: ced390a80ee860e80f984b2c8b31a8fedba37ad4
SHA256: 19fee6b5ee48896e3381dee2cd43d5ff0cfc637a000fc2fb24548eca807c275a
SHA512: 82c8e5d01472538ae0a694979cdc1b455e88475eb1f304150d8d6e02d511db2ad517fafe010c555c6d57bec9b3475159427919304fa8a558bc80de7668dda171
Homepage: https://github.com/facebookresearch/faiss
Description: efficient similarity search and clustering of dense vectors
Description-md5: 97a446d4f7c6eb17e90eec293f6aba51