How to Install and Uninstall python3-pyfastnoisesimd.x86_64 Package on Fedora 34
Last updated: November 16,2024
1. Install "python3-pyfastnoisesimd.x86_64" package
Please follow the steps below to install python3-pyfastnoisesimd.x86_64 on Fedora 34
$
sudo dnf update
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$
sudo dnf install
python3-pyfastnoisesimd.x86_64
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2. Uninstall "python3-pyfastnoisesimd.x86_64" package
Please follow the guidelines below to uninstall python3-pyfastnoisesimd.x86_64 on Fedora 34:
$
sudo dnf remove
python3-pyfastnoisesimd.x86_64
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$
sudo dnf autoremove
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3. Information about the python3-pyfastnoisesimd.x86_64 package on Fedora 34
Last metadata expiration check: 5:09:39 ago on Tue Sep 6 02:10:55 2022.
Available Packages
Name : python3-pyfastnoisesimd
Version : 0.4.2
Release : 1.fc34
Architecture : x86_64
Size : 211 k
Source : python-pyfastnoisesimd-0.4.2-1.fc34.src.rpm
Repository : fedora
Summary : Python Fast Noise with SIMD
URL : http://github.com/robbmcleod/pyfastnoisesimd
License : BSD
Description : PyFastNoiseSIMD is a wrapper around Jordan Peck's synthetic noise library which
: has been accelerated with SIMD instruction sets.
:
: Parallelism is further enhanced by the use of concurrent.futures to
: multi-thread the generation of noise for large arrays. Thread scaling is
: generally in the range of 50-90%, depending largely on the vectorized
: instruction set used. The number of threads, defaults to the number of virtual
: cores on the system.
Available Packages
Name : python3-pyfastnoisesimd
Version : 0.4.2
Release : 1.fc34
Architecture : x86_64
Size : 211 k
Source : python-pyfastnoisesimd-0.4.2-1.fc34.src.rpm
Repository : fedora
Summary : Python Fast Noise with SIMD
URL : http://github.com/robbmcleod/pyfastnoisesimd
License : BSD
Description : PyFastNoiseSIMD is a wrapper around Jordan Peck's synthetic noise library which
: has been accelerated with SIMD instruction sets.
:
: Parallelism is further enhanced by the use of concurrent.futures to
: multi-thread the generation of noise for large arrays. Thread scaling is
: generally in the range of 50-90%, depending largely on the vectorized
: instruction set used. The number of threads, defaults to the number of virtual
: cores on the system.