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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ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

Overview

• Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
• Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
• Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
• Extensibility: Easily extend functionality with custom plugins and configurations.
• Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
• Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

Features

• distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
  
• Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
• Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
• Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
• Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
• One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
• Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

Planned features (Wishlist / TODOs)

• Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

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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!

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Results: Infrastructure Achievements

We successfully built and automated a containerized stack to support our AI experiments. This included:

• a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
• Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
  • https://github.com/moio/TabletopGames/tree/hackweek24
  • https://github.com/moio/PyTAG/tree/hackweek24

./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

Results: Game Design Insights

Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

• Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
• AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
• Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
  • more about Bamboo on Dario's site
  • more about R3 on Silvio's site (italian, translation coming)
  • more about Totoro on Silvio's site

A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

Results: Learning, Collaboration, and Innovation

Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

• "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
• AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
• GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
• Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

The Context: AI + Board Games

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Symbol Relations by hli

Description

There are tools to build function call graphs based on parsing source code, for example, cscope.

This project aims to achieve a similar goal by directly parsing the disasembly (i.e. objdump) of a compiled binary. The assembly code is what the CPU sees, therefore more "direct". This may be useful in certain scenarios, such as gdb/crash debugging.

Detailed description and Demos can be found in the README file:

Supports x86 for now (because my customers only use x86 machines), but support for other architectures can be added easily.

Tested with python3.6

Goals

Any comments are welcome.

Resources

https://github.com/lhb-cafe/SymbolRelations

symrellib.py: mplements the symbol relation graph and the disassembly parser

symrel_tracer*.py: implements tracing (-t option)

symrel.py: "cli parser"

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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

• TensorFlow
• open-webui
• openQA instance of openSUSE
• openQA documentation

Timeline

Day 1

• Conversing with open-webui to teach me how to create a model based on openQA test results
  • Asking for example code using TensorFlow in Python
  • Discussing log files to explore what to analyze
  • Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant

Day 2

• Using NotebookLLM (Gemini) to produce conversational versions of blog posts
  • Writing openQA tests in Python
  • Developing in distrobox containers
• Researching the possibility of creating a project logo with AI
  • Asking open-webui, persons with prior experience and conducting a web search for advice

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

• Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
• Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.

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Framework laptop integration by nkrapp

Project Description

Although openSUSE does run on the Framework laptops out-of-the-box, there is still room to improve the experience. The ultimate goal is to get openSUSE on the list of community supported distros

Goal for this Hackweek

The goal this year is to at least package all of the soft- and firmware for accessories like the embedded controller, Framework 16 inputmodule and other tools. I already made some progress by packaging the inputmodule control software, but the firmware is still missing

Resources

As I only have a Framework laptop 16 and not a 13 I'm looking for people with hardware that can help me test

Progress:

Update 1:

The project lives under my home for now until I can get an independent project on OBS: Framework Laptop project

Also, the first package is already done, it's the cli for the led-matrix spacer module on the Framework Laptop 16. I am also testing this myself, but any feedback or questions are welcome.

You can test the package on the Framework 16 by adding this repo and installing the package inputmodule-control

Update 2:

I finished packaging the python cli/gui for the inputmodule. It is using a bit of a hack because one of the dependencies (PySimpleGUI) recently switched to a noncommercial license so I cannot ship it. But now you can actually play the games on the led-matrix (the rust package doesn't include controls for the games). I'm also working on the Framework system tools now, which should be more interesting for Framework 13 users.

You can test the package on the Framework 16 by installing python311-framework16_inputmodule and then running "ledmatrixctl" from the command line.

Update 3:

I packaged the framework_tool, a general application for interacting with the system. You can find it some detailed information what it can do here. On my system everything related to the embedded controller functionality doesn't work though, so some help testing and debugging would be appreciated.

Update 4:

Today I finished the qmk interface, which gives you a cli (and gui) to configure your Framework 16 keyboard. Sadly the Python gui is broken upstream, but I added the qmk_hid package with the cli and from my testing it works well.

Final Update:

All the interesting programs are now done, I decided to exclude the firmware for now since upstream also recommends using fwupd to update it. I will hack on more things related to the Framework Laptops in the future so if there are any ideas to improve the experience (or any bugs to report) feel free to message me about it.

As a final summary/help for everyone using a Framework Laptop who wants to use this software:

The source code for all packages can be found in repositories in the Framework organization on Github

All software can be installed from this repo (Tumbleweed)

The available packages are:

• framework-inputmodule-control (FW16) - play with the inputmodules on your Framework 16 (b1-display, led-matrix, c1-minimal)

• python-framework16_inputmodule (FW16) - same as inputmodule-control but is needed if you want to play and crontrol the built-in games in the led-matrix (call with ledmatrixctl or ledmatrixgui)

• framework_tool (FW13 and FW 16) - use to see and configure general things on your framework system. Commands using the embedded controller might not work, it looks like there are some problems with the kernel module used by the EC. Fixing this is out of scope for this hackweek but I am working on it

• qmk_hid (FW16) - a cli to configure the FW16 qmk keyboard. Sadly the gui for this is broken upstream so only the cli is usable for now

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Update Haskell ecosystem in Tumbleweed to GHC-9.10.x by psimons

Description

We are currently at GHC-9.8.x, which a bit old. So I'd like to take a shot at the latest version of the compiler, GHC-9.10.x. This is gonna be interesting because the new version requires major updates to all kinds of libraries and base packages, which typically means patching lots of packages to make them build again.

Goals

Have working builds of GHC-9.10.x and the required Haskell packages in 'devel:languages:haskell` so that we can compile:

• git-annex
• pandoc
• xmonad
• cabal-install

Resources

• https://build.opensuse.org/project/show/devel:languages:haskell/
• https://github.com/opensuse-haskell/configuration/
• #discuss-haskell
• https://www.twitch.tv/peti343

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Packaging Mu on OBS by joeyli

Description

Packaging Microsoft Mu project

Goals

Packaging Mu RPM on OBS.

Resources

https://microsoft.github.io/mu/

https://github.com/microsoft/mu

https://github.com/microsoft/mu_basecore

https://github.com/microsoft/mutianoplatforms

https://github.com/microsoft/mutianoplus

https://github.com/microsoft/mu_plus

Hackweek 22: Look at Microsoft Mu project

https://hackweek.opensuse.org/22/projects/look-at-microsoft-mu-project

https://drive.google.com/file/d/1BT31i7z3qh13adj9pdRz3lTUkqIsXvjY/view?usp=drive_link

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