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

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.

  • Comments

    Be the first to comment!

    Similar Projects

    Bugzilla goes AI - Phase 1 by nwalter

    Description

    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.

    Goals

    To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.

    Project Charter

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


    Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

    Description

    Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

    This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

    The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

    Goals

    By the end of Hack Week, we aim to have a single, working Python script that:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources


    SUSE Edge Image Builder MCP by eminguez

    Description

    Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.

    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.

    Goals

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

    Resources


    Background Coding Agent by mmanno

    Description

    I had only bad experiences with AI one-shots. However, monitoring agent work closely and interfering often did result in productivity gains.

    Now, other companies are using agents in pipelines. That makes sense to me, just like CI, we want to offload work to pipelines: Our engineering teams are consistently slowed down by "toil": low-impact, repetitive maintenance tasks. A simple linter rule change, a dependency bump, rebasing patch-sets on top of newer releases or API deprecation requires dozens of manual PRs, draining time from feature development.

    So far we have been writing deterministic, script-based automation for these tasks. And it turns out to be a common trap. These scripts are brittle, complex, and become a massive maintenance burden themselves.

    Can we make prompts and workflows smart enough to succeed at background coding?

    Goals

    We will build a platform that allows engineers to execute complex code transformations using prompts.

    By automating this toil, we accelerate large-scale migrations and allow teams to focus on high-value work.

    Our platform will consist of three main components:

    • "Change" Definition: Engineers will define a transformation as a simple, declarative manifest:
      • The target repositories.
      • A wrapper to run a "coding agent", e.g., "gemini-cli".
      • The task as a natural language prompt.
    • "Change" Management Service: A central service that orchestrates the jobs. It will receive Change definitions and be responsible for the job lifecycle.
    • Execution Runners: We could use existing sandboxed CI runners (like GitHub/GitLab runners) to execute each job or spawn a container.

    MVP

    • Define the Change manifest format.
    • Build the core Management Service that can accept and queue a Change.
    • Connect management service and runners, dynamically dispatch jobs to runners.
    • Create a basic runner script that can run a hard-coded prompt against a test repo and open a PR.

    Stretch Goals:

    • Multi-layered approach, Workflow Agents trigger Coding Agents:
      1. Workflow Agent: Gather information about the task interactively from the user.
      2. Coding Agent: Once the interactive agent has refined the task into a clear prompt, it hands this prompt off to the "coding agent." This background agent is responsible for executing the task and producing the actual pull request.
    • Use MCP:
      1. Workflow Agent gathers context information from Slack, Github, etc.
      2. Workflow Agent triggers a Coding Agent.
    • Create a "Standard Task" library with reliable prompts.
      1. Rebasing rancher-monitoring to a new version of kube-prom-stack
      2. Update charts to use new images
      3. Apply changes to comply with a new linter
      4. Bump complex Go dependencies, like k8s modules
      5. Backport pull requests to other branches
    • Add “review agents” that review the generated PR.

    See also


    "what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai

    Description

    Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.

    Goals

    • The user can run a command from the console, to check on a file or directory
    • The filemanager contains the "analyze" feature within the context menu
    • The local LLM could serve for other use cases where privacy matters

    TBD

    • Find or write capable one-shot and interactive MCP client
    • Find or write simple+secure file access MCP server
    • Create local LLM service with appropriate footprint, containerized
    • Shell command with options
    • KDE integration (Dolphin)
    • Package
    • Document

    Resources


    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):

    1. Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
    2. 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)
    3. Package management (install, remove, update...)
    4. Patching
    5. Applying any basic salt state (including a formula)
    6. Salt remote commands
    7. Bonus point: Java part for product identification, and monitoring enablement
    8. Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
    9. Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
    10. 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

    Seems to be a Debian 12 derivative, so adding it could be quite easy.

    • [ ] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
    • W] 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)


    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


    Uyuni Health-check Grafana Troubleshooter by ygutierrez

    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


    Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

    Description

    Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

    This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

    The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

    Goals

    By the end of Hack Week, we aim to have a single, working Python script that:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources


    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.


    Goals

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

    Resources