How to Install and Uninstall python311-opt-einsum Package on openSuSE Tumbleweed
Last updated: November 08,2024
1. Install "python311-opt-einsum" package
In this section, we are going to explain the necessary steps to install python311-opt-einsum on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python311-opt-einsum
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2. Uninstall "python311-opt-einsum" package
Please follow the step by step instructions below to uninstall python311-opt-einsum on openSuSE Tumbleweed:
$
sudo zypper remove
python311-opt-einsum
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3. Information about the python311-opt-einsum package on openSuSE Tumbleweed
Information for package python311-opt-einsum:
---------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-opt-einsum
Version : 3.3.0-3.1
Arch : noarch
Vendor : openSUSE
Installed Size : 664.8 KiB
Installed : No
Status : not installed
Source package : python-opt-einsum-3.3.0-3.1.src
Upstream URL : https://github.com/dgasmith/opt_einsum
Summary : Optimizing numpys einsum function
Description :
Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g.,
`np.einsum`,`dask.array.einsum`,`pytorch.einsum`,`tensorflow.einsum`)
by optimizing the expression's contraction order and dispatching many
operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized
einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch,
Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially
any library which conforms to a standard API. See the
[**documentation**](http://optimized-einsum.readthedocs.io) for more
information.
---------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-opt-einsum
Version : 3.3.0-3.1
Arch : noarch
Vendor : openSUSE
Installed Size : 664.8 KiB
Installed : No
Status : not installed
Source package : python-opt-einsum-3.3.0-3.1.src
Upstream URL : https://github.com/dgasmith/opt_einsum
Summary : Optimizing numpys einsum function
Description :
Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g.,
`np.einsum`,`dask.array.einsum`,`pytorch.einsum`,`tensorflow.einsum`)
by optimizing the expression's contraction order and dispatching many
operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized
einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch,
Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially
any library which conforms to a standard API. See the
[**documentation**](http://optimized-einsum.readthedocs.io) for more
information.