How to Install and Uninstall python312-opt-einsum Package on openSuSE Tumbleweed
Last updated: November 24,2024
1. Install "python312-opt-einsum" package
Here is a brief guide to show you how to install python312-opt-einsum on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python312-opt-einsum
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2. Uninstall "python312-opt-einsum" package
Here is a brief guide to show you how to uninstall python312-opt-einsum on openSuSE Tumbleweed:
$
sudo zypper remove
python312-opt-einsum
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3. Information about the python312-opt-einsum package on openSuSE Tumbleweed
Information for package python312-opt-einsum:
---------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python312-opt-einsum
Version : 3.3.0-3.1
Arch : noarch
Vendor : openSUSE
Installed Size : 595.8 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 : python312-opt-einsum
Version : 3.3.0-3.1
Arch : noarch
Vendor : openSUSE
Installed Size : 595.8 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.