How to Install and Uninstall python36-cachey Package on openSuSE Tumbleweed
Last updated: December 26,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python36-cachey" package
This guide let you learn how to install python36-cachey on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python36-cachey
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2. Uninstall "python36-cachey" package
Please follow the step by step instructions below to uninstall python36-cachey on openSuSE Tumbleweed:
$
sudo zypper remove
python36-cachey
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3. Information about the python36-cachey package on openSuSE Tumbleweed
Information for package python36-cachey:
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python36-cachey
Version : 0.2.1-1.6
Arch : noarch
Vendor : openSUSE
Installed Size : 28,6 KiB
Installed : No
Status : not installed
Source package : python-cachey-0.2.1-1.6.src
Summary : A Python cache mindful of computation/storage costs
Description :
Cachey tries to hold on to values that have the following characteristics
1. Expensive to recompute (in seconds)
2. Cheap to store (in bytes)
3. Frequently used
4. Recenty used
It accomplishes this by adding the following to each items score on each access
score += compute_time / num_bytes * (1 + eps) ** tick_time
For some small value of epsilon (which determines the memory halflife). This
has units of inverse bandwidth, has exponential decay of old results and
roughly linear amplification of repeated results.
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python36-cachey
Version : 0.2.1-1.6
Arch : noarch
Vendor : openSUSE
Installed Size : 28,6 KiB
Installed : No
Status : not installed
Source package : python-cachey-0.2.1-1.6.src
Summary : A Python cache mindful of computation/storage costs
Description :
Cachey tries to hold on to values that have the following characteristics
1. Expensive to recompute (in seconds)
2. Cheap to store (in bytes)
3. Frequently used
4. Recenty used
It accomplishes this by adding the following to each items score on each access
score += compute_time / num_bytes * (1 + eps) ** tick_time
For some small value of epsilon (which determines the memory halflife). This
has units of inverse bandwidth, has exponential decay of old results and
roughly linear amplification of repeated results.