How to Install and Uninstall python3-dask-distributed Package on openSUSE Leap
Last updated: November 22,2024
1. Install "python3-dask-distributed" package
Please follow the steps below to install python3-dask-distributed on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
python3-dask-distributed
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2. Uninstall "python3-dask-distributed" package
This guide covers the steps necessary to uninstall python3-dask-distributed on openSUSE Leap:
$
sudo zypper remove
python3-dask-distributed
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3. Information about the python3-dask-distributed package on openSUSE Leap
Information for package python3-dask-distributed:
-------------------------------------------------
Repository : Main Repository
Name : python3-dask-distributed
Version : 1.1.1-bp155.2.16
Arch : noarch
Vendor : openSUSE
Installed Size : 3.0 KiB
Installed : No
Status : not installed
Source package : python-dask-1.1.1-bp155.2.16.src
Upstream URL : http://github.com/ContinuumIO/dask/
Summary : Interface with the distributed task scheduler in dask
Description :
A minimal task scheduling abstraction and parallel arrays.
* dask is a specification to describe task dependency graphs.
* dask.array is a drop-in NumPy replacement (for a subset of NumPy) that
encodes blocked algorithms in dask dependency graphs.
* dask.async is a shared-memory asynchronous scheduler that efficiently
executes dask dependency graphs on multiple cores.
This package contains the dask distributed interface.
Dask.distributed is a lightweight library for distributed computing in
Python. It extends both the concurrent.futures and dask APIs to
moderate sized clusters.
-------------------------------------------------
Repository : Main Repository
Name : python3-dask-distributed
Version : 1.1.1-bp155.2.16
Arch : noarch
Vendor : openSUSE
Installed Size : 3.0 KiB
Installed : No
Status : not installed
Source package : python-dask-1.1.1-bp155.2.16.src
Upstream URL : http://github.com/ContinuumIO/dask/
Summary : Interface with the distributed task scheduler in dask
Description :
A minimal task scheduling abstraction and parallel arrays.
* dask is a specification to describe task dependency graphs.
* dask.array is a drop-in NumPy replacement (for a subset of NumPy) that
encodes blocked algorithms in dask dependency graphs.
* dask.async is a shared-memory asynchronous scheduler that efficiently
executes dask dependency graphs on multiple cores.
This package contains the dask distributed interface.
Dask.distributed is a lightweight library for distributed computing in
Python. It extends both the concurrent.futures and dask APIs to
moderate sized clusters.