How to Install and Uninstall tensorflow2-lite Package on openSuSE Tumbleweed
Last updated: December 25,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "tensorflow2-lite" package
This tutorial shows how to install tensorflow2-lite on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
tensorflow2-lite
Copied
2. Uninstall "tensorflow2-lite" package
Please follow the guidelines below to uninstall tensorflow2-lite on openSuSE Tumbleweed:
$
sudo zypper remove
tensorflow2-lite
Copied
3. Information about the tensorflow2-lite package on openSuSE Tumbleweed
Information for package tensorflow2-lite:
-----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : tensorflow2-lite
Version : 2.6.2-1.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 3,3 MiB
Installed : No
Status : not installed
Source package : tensorflow2-lite-2.6.2-1.2.src
Summary : A framework used for deep learning
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 : tensorflow2-lite
Version : 2.6.2-1.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 3,3 MiB
Installed : No
Status : not installed
Source package : tensorflow2-lite-2.6.2-1.2.src
Summary : A framework used for deep learning
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.