How to Install and Uninstall libtensorflow_framework2 Package on openSuSE Tumbleweed
Last updated: November 23,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "libtensorflow_framework2" package
In this section, we are going to explain the necessary steps to install libtensorflow_framework2 on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
libtensorflow_framework2
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2. Uninstall "libtensorflow_framework2" package
Here is a brief guide to show you how to uninstall libtensorflow_framework2 on openSuSE Tumbleweed:
$
sudo zypper remove
libtensorflow_framework2
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3. Information about the libtensorflow_framework2 package on openSuSE Tumbleweed
Information for package libtensorflow_framework2:
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Repository : openSUSE-Tumbleweed-Oss
Name : libtensorflow_framework2
Version : 2.6.2-1.3
Arch : x86_64
Vendor : openSUSE
Installed Size : 17,0 MiB
Installed : No
Status : not installed
Source package : tensorflow2-2.6.2-1.3.src
Summary : Shared library for tensorflow
Description :
This open source software library for numerical computation is used for data
flow graphs. The graph nodes represent mathematical operations, while the graph
edges represent the multidimensional data arrays (tensors) that flow between
them. This flexible architecture enables you to deploy computation to one or
more CPUs in a desktop, server, or mobile device without rewriting code.
-------------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libtensorflow_framework2
Version : 2.6.2-1.3
Arch : x86_64
Vendor : openSUSE
Installed Size : 17,0 MiB
Installed : No
Status : not installed
Source package : tensorflow2-2.6.2-1.3.src
Summary : Shared library for tensorflow
Description :
This open source software library for numerical computation is used for data
flow graphs. The graph nodes represent mathematical operations, while the graph
edges represent the multidimensional data arrays (tensors) that flow between
them. This flexible architecture enables you to deploy computation to one or
more CPUs in a desktop, server, or mobile device without rewriting code.