How to Install and Uninstall python38-azure-ai-metricsadvisor Package on openSuSE Tumbleweed
Last updated: December 25,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python38-azure-ai-metricsadvisor" package
This is a short guide on how to install python38-azure-ai-metricsadvisor on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python38-azure-ai-metricsadvisor
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2. Uninstall "python38-azure-ai-metricsadvisor" package
This tutorial shows how to uninstall python38-azure-ai-metricsadvisor on openSuSE Tumbleweed:
$
sudo zypper remove
python38-azure-ai-metricsadvisor
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3. Information about the python38-azure-ai-metricsadvisor package on openSuSE Tumbleweed
Information for package python38-azure-ai-metricsadvisor:
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Repository : openSUSE-Tumbleweed-Oss
Name : python38-azure-ai-metricsadvisor
Version : 1.0.0.0-1.2
Arch : noarch
Vendor : openSUSE
Installed Size : 3,7 MiB
Installed : No
Status : not installed
Source package : python-azure-ai-metricsadvisor-1.0.0.0-1.2.src
Summary : Microsoft Azure Metrics Advisor Client Library for Python
Description :
Metrics Advisor is a scalable real-time time series monitoring, alerting, and root cause analysis platform.
Use Metrics Advisor to:
* Analyze multi-dimensional data from multiple data sources
* Identify and correlate anomalies
* Configure and fine-tune the anomaly detection model used on your data
* Diagnose anomalies and help with root cause analysis
---------------------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-azure-ai-metricsadvisor
Version : 1.0.0.0-1.2
Arch : noarch
Vendor : openSUSE
Installed Size : 3,7 MiB
Installed : No
Status : not installed
Source package : python-azure-ai-metricsadvisor-1.0.0.0-1.2.src
Summary : Microsoft Azure Metrics Advisor Client Library for Python
Description :
Metrics Advisor is a scalable real-time time series monitoring, alerting, and root cause analysis platform.
Use Metrics Advisor to:
* Analyze multi-dimensional data from multiple data sources
* Identify and correlate anomalies
* Configure and fine-tune the anomaly detection model used on your data
* Diagnose anomalies and help with root cause analysis