Saline (state deployment control and monitoring tool for SUSE Manager/Uyuni) by vizhestkov

Project Description

Saline is an addition for salt used in SUSE Manager/Uyuni aimed to provide better control and visibility for states deploymend in the large scale environments.

In current state the published version can be used only as a Prometheus exporter and missing some of the key features implemented in PoC (not published). Now it can provide metrics related to salt events and state apply process on the minions. But there is no control on this process implemented yet.

Continue with implementation of the missing features and improve the existing implementation:

• authentication (need to decide how it should be/or not related to salt auth)

• web service providing the control of states deployment

Goal for this Hackweek

• Implement missing key features

• Implement the tool for state deployment control with CLI

Resources

https://github.com/openSUSE/saline

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Create SUSE Manager users from ldap/ad groups by mbrookhuis

Description

This tool is used to create users in SUSE Manager Server based on LDAP/AD groups. For each LDAP/AD group a role within SUSE Manager Server is defined. Also, the tool will check if existing users still have the role they should have, and, if not, it will be corrected. The same for if a user is disabled, it will be enabled again. If a users is not present in the LDAP/AD groups anymore, it will be disabled or deleted, depending on the configuration.

The code is written for Python 3.6 (the default with SLES15.x), but will also work with newer versions. And works against SUSE Manger 4.3 and 5.x

Goals

Create a python and/or golang utility that will manage users in SUSE Manager based on LDAP/AD group-membership. In a configuration file is defined which roles the members of a group will get.

Table of contents

• Installation
• Usage

Installation

To install this project, perform the following steps:

• Be sure that python 3.6 is installed and also the module python3-PyYAML. Also the ldap3 module is needed:

bash zypper in python3 python3-PyYAML pip install yaml

• On the server or PC, where it should run, create a directory. On linux, e.g. /opt/sm-ldap-users

• Copy all the file to this directory.

• Edit the configsm.yaml. All parameters should be entered. Tip: for the ldap information, the best would be to use the same as for SSSD.

• Be sure that the file sm-ldap-users.py is executable. It would be good to change the owner to root:root and only root can read and execute:

bash chmod 600 * chmod 700 sm-ldap-users.py chown root:root *

Usage

This is very simple. Once the configsm.yaml contains the correct information, executing the following will do the magic:

bash /sm-ldap-users.py

repository link

https://github.com/mbrookhuis/sm-ldap-users

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Improve Development Environment on Uyuni by mbussolotto

Description

Currently create a dev environment on Uyuni might be complicated. The steps are:

• add the correct repo
• download packages
• configure your IDE (checkstyle, format rules, sonarlint....)
• setup debug environment
• ...

The current doc can be improved: some information are hard to be find out, some others are completely missing.

Dev Container might solve this situation.

Goals

Uyuni development in no time:

• using VSCode:
  • setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
  • dev container should contains all dependencies
  • setup debug environment
• implement a GitHub Workspace solution
• re-write documentation

Lots of pieces are already implemented: we need to connect them in a consistent solution.

Resources

• https://github.com/uyuni-project/uyuni/wiki

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Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez

Description

Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.

Goals

• Explore Ollama
• Test different models
• Fine tuning
• Explore possible integration in Uyuni

Resources

• https://ollama.com/
• https://huggingface.co/
• https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/

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Automated Test Report reviewer by oscar-barrios

Description

In SUMA/Uyuni team we spend a lot of time reviewing test reports, analyzing each of the test cases failing, checking if the test is a flaky test, checking logs, etc.

Goals

Speed up the review by automating some parts through AI, in a way that we can consume some summary of that report that could be meaningful for the reviewer.

Resources

No idea about the resources yet, but we will make use of:

• HTML/JSON Report (text + screenshots)
• The Test Suite Status GithHub board (via API)
• The environment tested (via SSH)
• The test framework code (via files)

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