Everybody is talking about (and with) ChatGPT. I tried it and was impressed by how well the language model behaves and finally how real and humanly it appears, despite the obvious nonsense that it outputs. I was wondering how machine learning practically works and how to build a neural network.

Project Description

Learn about AI, ML, neural networks and get a better idea on limitations, risks and opportunities.

Goal for this Hackweek

Understand the concepts, create a demo case for machine learning with OS software.

Resources

Needs time in the first place to view documentation, and probably a Cray EX235a towards the end of the week :-)

Looking for hackers with the skills:

ai artificial-intelligence ml machine-learning neural

This project is part of:

Hack Week 22

Activity

  • almost 2 years ago: maritawerner joined this project.
  • almost 2 years ago: fgiudici liked this project.
  • almost 2 years ago: robert.richardson liked this project.
  • almost 2 years ago: maritawerner liked this project.
  • almost 2 years ago: rsimai started this project.
  • almost 2 years ago: rsimai added keyword "ml" to this project.
  • almost 2 years ago: rsimai added keyword "machine-learning" to this project.
  • almost 2 years ago: rsimai added keyword "neural" to this project.
  • almost 2 years ago: rsimai added keyword "ai" to this project.
  • almost 2 years ago: rsimai added keyword "artificial-intelligence" to this project.
  • almost 2 years ago: rsimai originated this project.

  • Comments

    • jjanes
      almost 2 years ago by jjanes | Reply

      I highly recommend the freely available course from FastAI for this group - https://www.fast.ai/

    • maritawerner
      almost 2 years ago by maritawerner | Reply

      Interesting Link: https://en.wikipedia.org/wiki/Hallucination(artificialintelligence)

    Similar Projects

    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!


    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/


    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


    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:
    • Setup a Web UI for it, choosing an easily extensible and customizable option between:
    • 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


    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

    • 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.


    COOTWbot by ngetahun

    Project Description

    At SCC, we have a rotating task of COOTW (Commanding Office of the Week). This task involves responding to customer requests from jira and slack help channels, monitoring production systems and doing small chores. Usually, we have documentation to help the COOTW answer questions and quickly find fixes. Most of these are distributed across github, trello and SUSE Support documentation. The aim of this project is to explore the magic of LLMs and create a conversational bot.

    Goal for this Hackweek

    • Build data ingestion Data source:
      • SUSE KB docs
      • scc github docs
      • scc trello knowledge board
    • Test out new RAG architecture

    • https://gitlab.suse.de/ngetahun/cootwbot