How to Install and Uninstall python3-bluepyopt.x86_64 Package on Fedora 38
Last updated: October 30,2024
1. Install "python3-bluepyopt.x86_64" package
Please follow the steps below to install python3-bluepyopt.x86_64 on Fedora 38
$
sudo dnf update
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$
sudo dnf install
python3-bluepyopt.x86_64
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2. Uninstall "python3-bluepyopt.x86_64" package
Please follow the step by step instructions below to uninstall python3-bluepyopt.x86_64 on Fedora 38:
$
sudo dnf remove
python3-bluepyopt.x86_64
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$
sudo dnf autoremove
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3. Information about the python3-bluepyopt.x86_64 package on Fedora 38
Last metadata expiration check: 0:13:34 ago on Sat Mar 16 16:59:57 2024.
Available Packages
Name : python3-bluepyopt
Version : 1.14.10
Release : 1.fc38
Architecture : x86_64
Size : 436 k
Source : python-bluepyopt-1.14.10-1.fc38.src.rpm
Repository : updates
Summary : Bluebrain Python Optimisation Library (bluepyopt)
URL : https://github.com/BlueBrain/BluePyOpt
License : LGPL-3.0-only
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
Version : 1.14.10
Release : 1.fc38
Architecture : x86_64
Size : 436 k
Source : python-bluepyopt-1.14.10-1.fc38.src.rpm
Repository : updates
Summary : Bluebrain Python Optimisation Library (bluepyopt)
URL : https://github.com/BlueBrain/BluePyOpt
License : LGPL-3.0-only
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.