How to Install and Uninstall libxgboost-dev Package on Ubuntu 21.10 (Impish Indri)

Last updated: April 30,2024

1. Install "libxgboost-dev" package

Please follow the steps below to install libxgboost-dev on Ubuntu 21.10 (Impish Indri)


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

This is a short guide on how to uninstall libxgboost-dev on Ubuntu 21.10 (Impish Indri):


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

Package: libxgboost-dev
Architecture: amd64
Version: 1.2.1-1
Priority: optional
Section: universe/libdevel
Source: xgboost
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Deep Learning Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 222
Depends: libxgboost0 (= 1.2.1-1)
Filename: pool/universe/x/xgboost/libxgboost-dev_1.2.1-1_amd64.deb
Size: 43824
MD5sum: fc75d9f573223ba5ae50b63d0198e236
SHA1: fda47655b6e142aa81fb17abd79cb7b50ef379c8
SHA256: d91c13fe38c38c4dc69ab85e51fe8d591a8bedf54c6f23d446d9c14701b9893d
SHA512: afad9855afb9f4f8b9d469992a72dd59af8c9ce9720b7a793d4d7f5a61b956653b38a06d5cd8ef769392f611e9eb2d2a99a0ac2b63ea608c01d84739b8e62885
Homepage: https://xgboost.ai/
Description-en: Scalable and Flexible Gradient Boosting (Development)
XGBoost is an optimized distributed gradient boosting library designed to be
highly efficient, flexible and portable. It implements machine learning
algorithms under the Gradient Boosting framework. XGBoost provides a parallel
tree boosting (also known as GBDT, GBM) that solve many data science problems
in a fast and accurate way. The same code runs on major distributed environment
(Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of
examples.
Description-md5: e8dd32d44bd43888da600d9ac19b816e