How to Install and Uninstall benchdnn Package on openSuSE Tumbleweed
Last updated: November 07,2024
1. Install "benchdnn" package
Please follow the instructions below to install benchdnn on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
benchdnn
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2. Uninstall "benchdnn" package
This tutorial shows how to uninstall benchdnn on openSuSE Tumbleweed:
$
sudo zypper remove
benchdnn
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3. Information about the benchdnn package on openSuSE Tumbleweed
Information for package benchdnn:
---------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : benchdnn
Version : 3.3.3-1.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 17.0 MiB
Installed : No
Status : not installed
Source package : onednn-3.3.3-1.2.src
Upstream URL : https://01.org/onednn
Summary : Header files of Intel Math Kernel Library
Description :
Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
open-source performance library for deep-learning applications. The library
accelerates deep-learning applications and frameworks on Intel architecture.
Intel MKL-DNN contains vectorized and threaded building blocks that you can use
to implement deep neural networks (DNN) with C and C++ interfaces.
This package only includes the benchmark utility including its input files.
---------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : benchdnn
Version : 3.3.3-1.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 17.0 MiB
Installed : No
Status : not installed
Source package : onednn-3.3.3-1.2.src
Upstream URL : https://01.org/onednn
Summary : Header files of Intel Math Kernel Library
Description :
Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
open-source performance library for deep-learning applications. The library
accelerates deep-learning applications and frameworks on Intel architecture.
Intel MKL-DNN contains vectorized and threaded building blocks that you can use
to implement deep neural networks (DNN) with C and C++ interfaces.
This package only includes the benchmark utility including its input files.