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 1 year ago: nadvornik added keyword "ai" to this project.
  • about 1 year ago: nadvornik added keyword "uyuni" to this project.
  • about 1 year ago: nadvornik originated this project.

  • Comments

    Be the first to comment!

    Similar Projects

    Save pytorch models in OCI registries by jguilhermevanz

    Description

    A prerequisite for running applications in a cloud environment is the presence of a container registry. Another common scenario is users performing machine learning workloads in such environments. However, these types of workloads require dedicated infrastructure to run properly. We can leverage these two facts to help users save resources by storing their machine learning models in OCI registries, similar to how we handle some WebAssembly modules. This approach will save users the resources typically required for a machine learning model repository for the applications they need to run.

    Goals

    Allow PyTorch users to save and load machine learning models in OCI registries.

    Resources


    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)


    SUSE AI Meets the Game Board by moio

    Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!
    A chameleon playing chess in a train car, as a metaphor of SUSE AI applied to games


    AI + Board Games

    Board games have long been fertile ground for AI innovation, pushing the boundaries of capabilities such as strategy, adaptability, and real-time decision-making - from Deep Blue's chess mastery to AlphaZero’s domination of Go. Games aren’t just fun: they’re complex, dynamic problems that often mirror real-world challenges, making them interesting from an engineering perspective.

    As avid board gamers, aspiring board game designers, and engineers with careers in open source infrastructure, we’re excited to dive into the latest AI techniques first-hand.

    Our goal is to develop an all-open-source, all-green AWS-based stack powered by some serious hardware to drive our board game experiments forward!


    Project Goals

    1. Set Up the Stack:

      • Install and configure the TAG and PyTAG frameworks on SUSE Linux Enterprise Base Container Images.
      • Integrate with the SUSE AI stack for GPU-accelerated training on AWS.
      • Validate a sample GPU-accelerated PyTAG workload on SUSE AI.
      • Ensure the setup is entirely repeatable with Terraform and configuration scripts, documenting results along the way.
    2. Design and Implement AI Agents:

      • Develop AI agents for the two board games, incorporating Statistical Forward Planning and Deep Reinforcement Learning techniques.
      • Fine-tune model parameters to optimize game-playing performance.
      • Document the advantages and limitations of each technique.
    3. Test, Analyze, and Refine:

      • Conduct AI vs. AI and AI vs. human matches to evaluate agent strategies and performance.
      • Record insights, document learning outcomes, and refine models based on real-world gameplay.

    Technical Stack

    • Frameworks: TAG and PyTAG for AI agent development
    • Platform: SUSE AI
    • Tools: AWS for high-performance GPU acceleration

    Why This Project Matters

    This project not only deepens our understanding of AI techniques by doing but also showcases the power and flexibility of SUSE’s open-source infrastructure for supporting high-level AI projects. By building on an all-open-source stack, we aim to create a pathway for other developers and AI enthusiasts to explore, experiment, and deploy their own innovative projects within the open-source space.


    Our Motivation

    We believe hands-on experimentation is the best teacher.

    Combining our engineering backgrounds with our passion for board games, we’ll explore AI in a way that’s both challenging and creatively rewarding. Our ultimate goal? To hack an AI agent that’s as strategic and adaptable as a real human opponent (if not better!) — and to leverage it to design even better games... for humans to play!


    Research how LLMs could help to Linux developers and/or users by anicka

    Description

    Large language models like ChatGPT have demonstrated remarkable capabilities across a variety of applications. However, their potential for enhancing the Linux development and user ecosystem remains largely unexplored. This project seeks to bridge that gap by researching practical applications of LLMs to improve workflows in areas such as backporting, packaging, log analysis, system migration, and more. By identifying patterns that LLMs can leverage, we aim to uncover new efficiencies and automation strategies that can benefit developers, maintainers, and end users alike.

    Goals

    • Evaluate Existing LLM Capabilities: Research and document the current state of LLM usage in open-source and Linux development projects, noting successes and limitations.
    • Prototype Tools and Scripts: Develop proof-of-concept scripts or tools that leverage LLMs to perform specific tasks like automated log analysis, assisting with backporting patches, or generating packaging metadata.
    • Assess Performance and Reliability: Test the tools' effectiveness on real-world Linux data and analyze their accuracy, speed, and reliability.
    • Identify Best Use Cases: Pinpoint which tasks are most suitable for LLM support, distinguishing between high-impact and impractical applications.
    • Document Findings and Recommendations: Summarize results with clear documentation and suggest next steps for potential integration or further development.

    Resources

    • Local LLM Implementations: Access to locally hosted LLMs such as LLaMA, GPT-J, or similar open-source models that can be run and fine-tuned on local hardware.
    • Computing Resources: Workstations or servers capable of running LLMs locally, equipped with sufficient GPU power for training and inference.
    • Sample Data: Logs, source code, patches, and packaging data from openSUSE or SUSE repositories for model training and testing.
    • Public LLMs for Benchmarking: Access to APIs from platforms like OpenAI or Hugging Face for comparative testing and performance assessment.
    • Existing NLP Tools: Libraries such as spaCy, Hugging Face Transformers, and PyTorch for building and interacting with local LLMs.
    • Technical Documentation: Tutorials and resources focused on setting up and optimizing local LLMs for tasks relevant to Linux development.
    • Collaboration: Engagement with community experts and teams experienced in AI and Linux for feedback and joint exploration.


    Make more sense of openQA test results using AI by livdywan

    Description

    AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.

    User Story

    Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?

    Goals

    • Leverage a chat interface to help Allison
    • Create a model from scratch based on data from openQA
    • Proof of concept for automated analysis of openQA test results

    Bonus

    • Use AI to suggest solutions to merge conflicts
      • This would need a merge conflict editor that can suggest solving the conflict
    • Use image recognition for needles

    Resources

    Timeline

    Day 1

    • Conversing with open-webui to teach me how to create a model based on openQA test results

    Day 2

    Highlights

    • I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
    • Convincing the chat interface to produce code specific to my use case required very explicit instructions.
    • Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
    • Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses

    Outcomes


    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

    FUSS

    FUSS is a complete GNU/Linux solution (server, client and desktop/standalone) based on Debian for managing an educational network.

    https://fuss.bz.it/

    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)
    • [ ] 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 (if patch information is available, could require writing some code to parse it, but IIRC we have support for Ubuntu already)
    • [ ] Applying any basic salt state (including a formula)
    • [ ] Salt remote commands
    • [ ] Bonus point: Java part for product identification, and monitoring enablement


    Uyuni developer-centric documentation by deneb_alpha

    Description

    While we currently have extensive documentation on user-oriented tasks such as adding minions, patching, fine-tuning, etc, there is a notable gap when it comes to centralizing and documenting core functionalities for developers.

    The number of functionalities and side tools we have in Uyuni can be overwhelming. It would be nice to have a centralized place with descriptive list of main/core functionalities.

    Goals

    Create, aggregate and review on the Uyuni wiki a set of resources, focused on developers, that include also some known common problems/troubleshooting.

    The documentation will be helpful not only for everyone who is trying to learn the functionalities with all their inner processes like newcomer developers or community enthusiasts, but also for anyone who need a refresh.

    Resources

    The resources are currently aggregated here: https://github.com/uyuni-project/uyuni/wiki


    Saltboot ability to deploy OEM images by oholecek

    Description

    Saltboot is a system deployment part of Uyuni. It is the mechanism behind deploying Kiwi built system images from central Uyuni server location.

    System image is when the image is only of one partition and does not contain whole disk image and deployment system has to take care of partitioning, fstab on top of integrity validation.

    However systems like Aeon, SUSE Linux Enterprise Micro and similar are distributed as disk images (also so called OEM images). Saltboot currently cannot deploy these systems.

    The main problem to saltboot is however that currently saltboot support is built into the image itself. This step is not desired when using OEM images.

    Goals

    Saltboot needs to be standalone and be able to deploy OEM images. Responsibility of saltboot would then shrink to selecting correct image, image integrity validation, deployment and boot to deployed system.

    Resources

    • Saltboot - https://github.com/uyuni-project/retail/tree/master
    • Uyuni - https://github.com/uyuni-project/uyuni


    Edge Image Builder and mkosi for Uyuni by oholecek

    Description

    One part of Uyuni system management tool is ability to build custom images. Currently Uyuni supports only Kiwi image builder.

    Kiwi however is not the only image building system out there and with the goal to also become familiar with other systems, this projects aim to add support for Edge Image builder and systemd's mkosi systems.

    Goals

    Uyuni is able to

    • provision EIB and mkosi build hosts
    • build EIB and mkosi images and store them

    Resources

    • Uyuni - https://github.com/uyuni-project/uyuni
    • Edge Image builder - https://github.com/suse-edge/edge-image-builder
    • mkosi - https://github.com/systemd/mkosi


    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