How to Install and Uninstall python3-cachey Package on openSUSE Leap
Last updated: November 23,2024
1. Install "python3-cachey" package
Please follow the instructions below to install python3-cachey on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
python3-cachey
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2. Uninstall "python3-cachey" package
In this section, we are going to explain the necessary steps to uninstall python3-cachey on openSUSE Leap:
$
sudo zypper remove
python3-cachey
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3. Information about the python3-cachey package on openSUSE Leap
Information for package python3-cachey:
---------------------------------------
Repository : Main Repository
Name : python3-cachey
Version : 0.2.1-bp155.2.10
Arch : noarch
Vendor : openSUSE
Installed Size : 29.0 KiB
Installed : No
Status : not installed
Source package : python-cachey-0.2.1-bp155.2.10.src
Upstream URL : http://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 : Main Repository
Name : python3-cachey
Version : 0.2.1-bp155.2.10
Arch : noarch
Vendor : openSUSE
Installed Size : 29.0 KiB
Installed : No
Status : not installed
Source package : python-cachey-0.2.1-bp155.2.10.src
Upstream URL : http://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.