How to Install and Uninstall python3-bluepyopt-doc.noarch Package on Fedora 35
Last updated: January 16,2025
1. Install "python3-bluepyopt-doc.noarch" package
Please follow the guidelines below to install python3-bluepyopt-doc.noarch on Fedora 35
$
sudo dnf update
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$
sudo dnf install
python3-bluepyopt-doc.noarch
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2. Uninstall "python3-bluepyopt-doc.noarch" package
This guide covers the steps necessary to uninstall python3-bluepyopt-doc.noarch on Fedora 35:
$
sudo dnf remove
python3-bluepyopt-doc.noarch
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$
sudo dnf autoremove
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3. Information about the python3-bluepyopt-doc.noarch package on Fedora 35
Last metadata expiration check: 1:33:13 ago on Wed Sep 7 08:25:01 2022.
Available Packages
Name : python3-bluepyopt-doc
Version : 1.11.15
Release : 1.fc35
Architecture : noarch
Size : 4.5 M
Source : python-bluepyopt-1.11.15-1.fc35.src.rpm
Repository : updates
Summary : Documentation for bluepyopt
URL : https://github.com/BlueBrain/BluePyOpt
License : LGPLv3
Description : The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible
: framework for data-driven model parameter optimisation that wraps and
: standardises several existing open-source tools. It simplifies the task of
: creating and sharing these optimisations, and the associated techniques and
: knowledge. This is achieved by abstracting the optimisation and evaluation
: tasks into various reusable and flexible discrete elements according to
: established best-practices.
Available Packages
Name : python3-bluepyopt-doc
Version : 1.11.15
Release : 1.fc35
Architecture : noarch
Size : 4.5 M
Source : python-bluepyopt-1.11.15-1.fc35.src.rpm
Repository : updates
Summary : Documentation for bluepyopt
URL : https://github.com/BlueBrain/BluePyOpt
License : LGPLv3
Description : The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible
: framework for data-driven model parameter optimisation that wraps and
: standardises several existing open-source tools. It simplifies the task of
: creating and sharing these optimisations, and the associated techniques and
: knowledge. This is achieved by abstracting the optimisation and evaluation
: tasks into various reusable and flexible discrete elements according to
: established best-practices.