Project Description

The goal is to have a language model, that is able to answer technical questions on Uyuni. Uyuni documentation is too large for in-context processing, so finetuning is the way to go.

Goal for this Hackweek

Finetune a model based on llama-2-7b.

Resources

github repo

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Looking for hackers with the skills:

ai uyuni

This project is part of:

Hack Week 23

Activity

about 2 years ago: nadvornik added keyword "ai" to this project.

about 2 years ago: nadvornik added keyword "uyuni" to this project.

about 2 years ago: nadvornik originated this project.

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Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.

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Familiarize myself with MCPs
Unrealistic: Have an MCP that can generate an EIB config file

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This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.

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https://docs.google.com/document/d/1HbAvgrg8T3pd1FIx74nEfCObCljpO77zz5In_Jpw4as/edit?usp=sharing## Description

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I want to setup a Radeon RX 9600 XT 16 GB at home with ROCm on Slowroll.

Goals

I want to test how fast AI inference can get with the GPU and if I can use LoRA to re-train an existing free model for some task.

Resources

https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html
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Results

got inference working with llama.cpp:

export LLAMACPP_ROCM_ARCH=gfx1200
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$LLAMACPP_ROCM_ARCH \
-DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
-Dhipblas_DIR=/usr/lib64/cmake/hipblaslt/ \
&amp;&amp; cmake --build build --config Release -j8
m=models/gpt-oss-20b-mxfp4.gguf
cd $P/llama.cpp &amp;&amp; build/bin/llama-server --model $m --threads 8 --port 8005 --host 0.0.0.0 --device ROCm0 --n-gpu-layers 999

Without the --device option it faulted. Maybe because my APU also appears there?

I updated/fixed various related packages: https://src.opensuse.org/ROCm/rocm-examples/pulls/1 https://src.opensuse.org/ROCm/hipblaslt/pulls/1 SR 1320959

I benchmarked inference with llama.cpp + gpt-oss-20b-mxfp4.gguf and ROCm offloading to a Radeon RX 9060 XT 16GB. I varied the number of layers that went to the GPU:

0 layers 14.49 tokens/s (8 CPU cores)
9 layers 17.79 tokens/s 34% VRAM
15 layers 22.39 tokens/s 51% VRAM
20 layers 27.49 tokens/s 64% VRAM
24 layers 41.18 tokens/s 74% VRAM
25+ layers 86.63 tokens/s 75% VRAM (only 200% CPU load)

So there is a significant performance-boost if the whole model fits into the GPU's VRAM.

Gemini-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov

Motivation

What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?

Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?

Description

To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

Goals

Set up a Python environment

Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

Build the Dialogue Loop

Write a basic Python script using the Gemini API.
Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

Socratic/Systemic Strategy Implementation

Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
Implement Bugzillla call to collect the
Implement Questioning Framework as LLVM pre-conditioning
Define set of instructions
Assemble the Tool

Resources

What are Systemic Questions?

Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.

Gitlab Project

gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question

Enable more features in mcp-server-uyuni by j_renner

Description

I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:

Goals

Set up test environment locally with the MCP server and client + a recent MLM server and 1-2 managed clients
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Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil

Join the Gitter channel! https://gitter.im/uyuni-project/hackweek

Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!

Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.

For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.

No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)

The idea is testing Salt and Salt-ssh clients, but NOT traditional clients, which are deprecated.

To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):

Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
Package management (install, remove, update...)
Patching
Applying any basic salt state (including a formula)
Salt remote commands
Bonus point: Java part for product identification, and monitoring enablement
Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)

If you don't have knowledge about some of the steps: ask the team
If you still don't know what to do: switch to another distribution and keep testing.

This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)

Pending

Debian 13

The new version of the beloved Debian GNU/Linux OS

[W] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
[ ] Onboarding (salt minion from UI, salt minion from bootstrap script, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
[ ] Package management (install, remove, update...)
[ ] Patching (if patch information is available, could require writing some code to parse it, but IIRC we have support for Ubuntu already). Probably not for Debian as IIRC we don't support patches yet.
[ ] Applying any basic salt state (including a formula)
[ ] Salt remote commands
[ ] Bonus point: Java part for product identification, and monitoring enablement
[ ] Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
[ ] Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
[ ] Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

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Description

This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

Goals

Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
Evaluate support for local LLMs and external APIs through the plugin.
Evaluate if and how the Uyuni MCP server could be integrated as another source of information.

Resources

Grafana LMM plug-in

Uyuni Health-check

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Description

We already have initial integration of Ansible in Salt with the possibility to run playbooks from the salt-master on the salt-minion used as an Ansible Control node.

In this project I want to check if it possible to make Ansible working on the transport of Salt. Basically run playbooks with Ansible through existing established Salt (ZeroMQ) transport and not using ssh at all.

Goals

[v] Prepare the testing environment with Salt and Ansible installed
[v] Discover Ansible codebase to figure out possible ways of integration
[v] Create Salt/Uyuni inventory module
[v] Make basic modules to work with no using separate ssh connection, but reusing existing Salt connection
[v] Test some most basic playbooks

Resources

TBD

Uyuni read-only replica by cbosdonnat

Description

For now, there is no possible HA setup for Uyuni. The idea is to explore setting up a read-only shadow instance of an Uyuni and make it as useful as possible.

Possible things to look at:

live sync of the database, probably using the WAL. Some of the tables may have to be skipped or some features disabled on the RO instance (taskomatic, PXT sessions…)
Can we use a load balancer that routes read-only queries to either instance and the other to the RW one? For example, packages or PXE data can be served by both, the API GET requests too. The rest would be RW.

Goals

Prepare a document explaining how to do it.
PR with the needed code changes to support it

Move Uyuni Test Framework from Selenium to Playwright + AI by oscar-barrios

Description

This project aims to migrate the existing Uyuni Test Framework from Selenium to Playwright. The move will improve the stability, speed, and maintainability of our end-to-end tests by leveraging Playwright's modern features. We'll be rewriting the current Selenium code in Ruby to Playwright code in TypeScript, which includes updating the test framework runner, step definitions, and configurations. This is also necessary because we're moving from Cucumber Ruby to CucumberJS.

If you're still curious about the AI in the title, it was just a way to grab your attention. Thanks for your understanding.

Nah, let's be honest AI helped a lot to vibe code a good part of the Ruby methods of the Test framework, moving them to Typescript, along with the migration from Capybara to Playwright. I've been using "Cline" as plugin for WebStorm IDE, using Gemini API behind it.

Goals

Migrate Core tests including Onboarding of clients
Improve test reliabillity: Measure and confirm a significant reduction of flakiness.
Implement a robust framework: Establish a well-structured and reusable Playwright test framework using the CucumberJS