Image generation AI in container using Radeon GPU
an invention by tjyrinki_suse
Updated about 2 years ago. 5 hacker ♥️. 2 followers.

Project Description

I have casually investigated that recent open source image generation AI systems are relatively invasive of the host system if one starts to install them that way. Usually container is better but needs special configuration to access the needed hardware. I'd like to run something in a container utilizing the RDNA2 Radeon gfx card I have on my desktop computer.

The exact container type would be evaluated, and of course existing solutions will be seeked.

Goal for this Hackweek

The goals for the Hackweek include to have suitable optimized container that can be created from scratch with one command and can generate SUSE related images with the AMD graphics with 8GB RAM (which is a bit limited apparently).

Resources

https://github.com/tjyrinki/sd-rocm

Results

See the github link above, images below and the blog post at https://timojyrinki.gitlab.io/hugo/post/2023-02-02-stablediffusion-docker/

Looking for hackers with the skills:

gpu containers ai amd radeon rdna2

This project is part of:

Hack Week 22

Activity

  • about 2 years ago: punkioudi liked this project.
  • about 2 years ago: tjyrinki_suse started this project.
  • about 2 years ago: pdostal liked this project.
  • about 2 years ago: ilausuch liked this project.
  • about 2 years ago: dancermak liked this project.
  • about 2 years ago: tschmitz liked this project.
  • about 2 years ago: tjyrinki_suse added keyword "rdna2" to this project.
  • about 2 years ago: tjyrinki_suse added keyword "gpu" to this project.
  • about 2 years ago: tjyrinki_suse added keyword "containers" to this project.
  • about 2 years ago: tjyrinki_suse added keyword "ai" to this project.
  • about 2 years ago: tjyrinki_suse added keyword "amd" to this project.
  • about 2 years ago: tjyrinki_suse added keyword "radeon" to this project.
  • about 2 years ago: tjyrinki_suse originated this project.


    • Comments

    • tjyrinki_suse
      about 2 years ago by tjyrinki_suse | Reply

      Blog post at https://timojyrinki.gitlab.io/hugo/post/2023-02-02-stablediffusion-docker/ – read more there!

      See the git repo for what has been done as part of this project.

      example image

    • tjyrinki_suse
      about 2 years ago by tjyrinki_suse | Reply

      example image 2

    Similar Projects

    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

    Improve Development Environment on Uyuni by mbussolotto

    Description

    Currently create a dev environment on Uyuni might be complicated. The steps are:

    • add the correct repo
    • download packages
    • configure your IDE (checkstyle, format rules, sonarlint....)
    • setup debug environment
    • ...

    The current doc can be improved: some information are hard to be find out, some others are completely missing.

    Dev Container might solve this situation.

    Goals

    Uyuni development in no time:

    • using VSCode:
      • setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
      • dev container should contains all dependencies
      • setup debug environment
    • implement a GitHub Workspace solution
    • re-write documentation

    Lots of pieces are already implemented: we need to connect them in a consistent solution.

    Resources

    • https://github.com/uyuni-project/uyuni/wiki

    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

    Results: Infrastructure Achievements

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

    A screenshot of k9s and nvtop showing PyTAG running in Kubernetes with GPU acceleration

    ./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 .

    Cards from the three games

    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

    Port the classic browser game HackTheNet to PHP 8 by dgedon

    Description

    The classic browser game HackTheNet from 2004 still runs on PHP 4/5 and MySQL 5 and needs a port to PHP 8 and e.g. MariaDB.

    Goals

    • Port the game to PHP 8 and MariaDB 11
    • Create a container where the game server can simply be started/stopped

    Resources

    • https://github.com/nodeg/hackthenet

    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

    Technical talks at universities by agamez

    Description

    This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

    For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

    Goals

    • Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
    • Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
    • Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

    Resources

    • Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
    • SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.

    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.

    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

    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.

    AI for product management by a_jaeger

    Description

    Learn about AI and how it can help myself

    What are the jobs that a PM does where AI can help - and how?

    Goals

    • Investigate how AI can help with different tasks
    • Check out different AI tools, which one is best for which job
    • Summarize learning

    Resources

    • Reading some blog posts by PMs that looked into it
    • Popular and less popular AI tools

    Work is done SUSE internally at https://confluence.suse.com/display/~a_jaeger/Hackweek+25+-+AI+for+a+PM and subpages.

    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