How to Install and Uninstall python39-cachey Package on openSuSE Tumbleweed
Last updated: November 22,2024
1. Install "python39-cachey" package
Please follow the instructions below to install python39-cachey on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python39-cachey
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2. Uninstall "python39-cachey" package
This tutorial shows how to uninstall python39-cachey on openSuSE Tumbleweed:
$
sudo zypper remove
python39-cachey
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3. Information about the python39-cachey package on openSuSE Tumbleweed
Information for package python39-cachey:
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python39-cachey
Version : 0.2.1-2.8
Arch : noarch
Vendor : openSUSE
Installed Size : 22.1 KiB
Installed : No
Status : not installed
Source package : python-cachey-0.2.1-2.8.src
Upstream URL : https://github.com/mrocklin/cachey/
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 : python39-cachey
Version : 0.2.1-2.8
Arch : noarch
Vendor : openSUSE
Installed Size : 22.1 KiB
Installed : No
Status : not installed
Source package : python-cachey-0.2.1-2.8.src
Upstream URL : https://github.com/mrocklin/cachey/
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.