How to Install and Uninstall tensorflow_1_15_4-gnu-openmpi2-hpc Package on openSUSE Leap
Last updated: December 24,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "tensorflow_1_15_4-gnu-openmpi2-hpc" package
This guide covers the steps necessary to install tensorflow_1_15_4-gnu-openmpi2-hpc on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
tensorflow_1_15_4-gnu-openmpi2-hpc
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2. Uninstall "tensorflow_1_15_4-gnu-openmpi2-hpc" package
This is a short guide on how to uninstall tensorflow_1_15_4-gnu-openmpi2-hpc on openSUSE Leap:
$
sudo zypper remove
tensorflow_1_15_4-gnu-openmpi2-hpc
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3. Information about the tensorflow_1_15_4-gnu-openmpi2-hpc package on openSUSE Leap
Information for package tensorflow_1_15_4-gnu-openmpi2-hpc:
-----------------------------------------------------------
Repository : Main Repository
Name : tensorflow_1_15_4-gnu-openmpi2-hpc
Version : 1.15.4-bp153.1.78
Arch : x86_64
Vendor : openSUSE
Installed Size : 515,7 MiB
Installed : No
Status : not installed
Source package : tensorflow_1_15_4-gnu-openmpi2-hpc-1.15.4-bp153.1.78.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 : Main Repository
Name : tensorflow_1_15_4-gnu-openmpi2-hpc
Version : 1.15.4-bp153.1.78
Arch : x86_64
Vendor : openSUSE
Installed Size : 515,7 MiB
Installed : No
Status : not installed
Source package : tensorflow_1_15_4-gnu-openmpi2-hpc-1.15.4-bp153.1.78.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.