How to Install and Uninstall python38-Theano Package on openSuSE Tumbleweed
Last updated: November 27,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python38-Theano" package
Please follow the guidelines below to install python38-Theano on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
python38-Theano
Copied
2. Uninstall "python38-Theano" package
Please follow the step by step instructions below to uninstall python38-Theano on openSuSE Tumbleweed:
$
sudo zypper remove
python38-Theano
Copied
3. Information about the python38-Theano package on openSuSE Tumbleweed
Information for package python38-Theano:
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-Theano
Version : 1.0.5-3.3
Arch : noarch
Vendor : openSUSE
Installed Size : 21,9 MiB
Installed : No
Status : not installed
Source package : python-Theano-1.0.5-3.3.src
Summary : A scientific python library
Description :
Theano is a Python library that allows you to define, optimize, and
evaluate mathematical expressions involving multi-dimensional arrays.
Theano features:
* tight integration with numpy - Use numpy.ndarray in Theano-compiled
functions.
* transparent use of a GPU
* symbolic differentiation - Let Theano do your derivatives.
* speed and stability optimizations – Get the right answer for log(1+x)
even when x is really tiny.
* dynamic C code generation - Evaluate expressions faster.
* extensive unit-testing and self-verification – Detect and diagnose
many types of mistake.
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-Theano
Version : 1.0.5-3.3
Arch : noarch
Vendor : openSUSE
Installed Size : 21,9 MiB
Installed : No
Status : not installed
Source package : python-Theano-1.0.5-3.3.src
Summary : A scientific python library
Description :
Theano is a Python library that allows you to define, optimize, and
evaluate mathematical expressions involving multi-dimensional arrays.
Theano features:
* tight integration with numpy - Use numpy.ndarray in Theano-compiled
functions.
* transparent use of a GPU
* symbolic differentiation - Let Theano do your derivatives.
* speed and stability optimizations – Get the right answer for log(1+x)
even when x is really tiny.
* dynamic C code generation - Evaluate expressions faster.
* extensive unit-testing and self-verification – Detect and diagnose
many types of mistake.