How to Install and Uninstall python39-torch Package on openSuSE Tumbleweed
Last updated: November 23,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python39-torch" package
Please follow the step by step instructions below to install python39-torch on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python39-torch
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2. Uninstall "python39-torch" package
Please follow the guidelines below to uninstall python39-torch on openSuSE Tumbleweed:
$
sudo zypper remove
python39-torch
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3. Information about the python39-torch package on openSuSE Tumbleweed
Information for package python39-torch:
---------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python39-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 : python39-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