How to Install and Uninstall python38-torch Package on openSuSE Tumbleweed
Last updated: December 28,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python38-torch" package
This guide let you learn how to install python38-torch on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python38-torch
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2. Uninstall "python38-torch" package
Please follow the guidance below to uninstall python38-torch on openSuSE Tumbleweed:
$
sudo zypper remove
python38-torch
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3. Information about the python38-torch package on openSuSE Tumbleweed
Information for package python38-torch:
---------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-torch
Version : 1.5.1-6.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 92,1 MiB
Installed : No
Status : not installed
Source package : python-torch-1.5.1-6.2.src
Summary : Deep learning framework aka pytorch/Caffe2
Description :
PyTorch enables fast, flexible experimentation and efficient production through
a hybrid front-end, distributed training, and ecosystem of tools and libraries.
The library is developed by Facebook and other groups.
PyTorch provides two high-level features:
* Tensor computing (like NumPy) with strong acceleration via graphics
* processing units (GPU) Deep neural networks built on a tape-based autodiff
system
---------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-torch
Version : 1.5.1-6.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 92,1 MiB
Installed : No
Status : not installed
Source package : python-torch-1.5.1-6.2.src
Summary : Deep learning framework aka pytorch/Caffe2
Description :
PyTorch enables fast, flexible experimentation and efficient production through
a hybrid front-end, distributed training, and ecosystem of tools and libraries.
The library is developed by Facebook and other groups.
PyTorch provides two high-level features:
* Tensor computing (like NumPy) with strong acceleration via graphics
* processing units (GPU) Deep neural networks built on a tape-based autodiff
system