How to Install and Uninstall python2-azure-ai-metricsadvisor Package on openSUSE Leap
Last updated: December 25,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python2-azure-ai-metricsadvisor" package
This tutorial shows how to install python2-azure-ai-metricsadvisor on openSUSE Leap
$
sudo zypper refresh
Copied
$
sudo zypper install
python2-azure-ai-metricsadvisor
Copied
2. Uninstall "python2-azure-ai-metricsadvisor" package
Please follow the steps below to uninstall python2-azure-ai-metricsadvisor on openSUSE Leap:
$
sudo zypper remove
python2-azure-ai-metricsadvisor
Copied
3. Information about the python2-azure-ai-metricsadvisor package on openSUSE Leap
Information for package python2-azure-ai-metricsadvisor:
--------------------------------------------------------
Repository : Main Repository
Name : python2-azure-ai-metricsadvisor
Version : 1.0.0b1-3.3.1
Arch : noarch
Vendor : SUSE LLC
Installed Size : 1,8 MiB
Installed : No
Status : not installed
Source package : python-azure-ai-metricsadvisor-1.0.0b1-3.3.1.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 : Main Repository
Name : python2-azure-ai-metricsadvisor
Version : 1.0.0b1-3.3.1
Arch : noarch
Vendor : SUSE LLC
Installed Size : 1,8 MiB
Installed : No
Status : not installed
Source package : python-azure-ai-metricsadvisor-1.0.0b1-3.3.1.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