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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Use AI tools to convert legacy perl scripts to bash by nadvornik

Description

Use AI tools to convert legacy perl scripts to bash

Goals

Uyuni project contains legacy perl scripts used for setup. The perl dependency could be removed, to reduce the container size. The goal of this project is to research use of AI tools for this task.

Resources

Aider

Results:

Aider is not the right tool for this. It works ok for small changes, but not for complete rewrite from one language to another.

I got better results with direct API use from script.

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ghostwrAIter - a local AI assisted tool for helping with support cases by paolodepa

Description

This project is meant to fight the loneliness of the support team members, providing them an AI assistant (hopefully) capable of scraping supportconfigs in a RAG fashion, trying to answer specific questions.

Goals

• Setup an Ollama backend, spinning one (or more??) code-focused LLMs selected by license, performance and quality of the results between:
  • deepseek-coder-v2
  • dolphin-mistral
  • starcoder2
  • (...others??)
    
• Setup a Web UI for it, choosing an easily extensible and customizable option between:
  • openwebui
  • localAI
  • lobachat
  • (...others??)
    
• Extend the solution in order to be able to:
  • Add ZIU/Concord shared folders to its RAG context
  • Add BZ cases, splitted in comments to its RAG context
    • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query BZ
  • Add specific packages picking them from IBS repos
    • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query IBS
    • A plus would be to desume the packages of interest and the right channel and version to be picked from the added BZ cases

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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

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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.

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