How to Install and Uninstall python3-Theano Package on openSUSE Leap
Last updated: March 10,2025
1. Install "python3-Theano" package
This guide covers the steps necessary to install python3-Theano on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
python3-Theano
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2. Uninstall "python3-Theano" package
Learn how to uninstall python3-Theano on openSUSE Leap:
$
sudo zypper remove
python3-Theano
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3. Information about the python3-Theano package on openSUSE Leap
Information for package python3-Theano:
---------------------------------------
Repository : Main Repository
Name : python3-Theano
Version : 1.0.4-bp155.2.14
Arch : noarch
Vendor : openSUSE
Installed Size : 22.5 MiB
Installed : No
Status : not installed
Source package : python-Theano-1.0.4-bp155.2.14.src
Upstream URL : https://github.com/Theano/Theano
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 : Main Repository
Name : python3-Theano
Version : 1.0.4-bp155.2.14
Arch : noarch
Vendor : openSUSE
Installed Size : 22.5 MiB
Installed : No
Status : not installed
Source package : python-Theano-1.0.4-bp155.2.14.src
Upstream URL : https://github.com/Theano/Theano
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.