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/
This project is part of:
Hack Week 22
Activity
Comments
-
almost 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.
-
almost 2 years ago by tjyrinki_suse | Reply
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
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!
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):
./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
Enable the containerized Uyuni server to run on different host OS by j_renner
Description
The Uyuni server is provided as a container, but we still require it to run on Leap Micro? This is not how people expect to use containerized applications, so it would be great if we tested other host OSs and enabled them by providing builds of necessary tools for (e.g. mgradm). Interesting candidates should be:
- openSUSE Leap
- Cent OS 7
- Ubuntu
- ???
Goals
Make it really easy for anyone to run the Uyuni containerized server on whatever OS they want (with support for containers of course).
ADS-B receiver with MicroOS by epaolantonio
I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time?
There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090
) and web frontend (tar1090
).
Goals
- Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
- Make it easy to install
- Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)
Resources
- 1x Small Board Computer capable of running MicroOS
- 1x RTL2832U DVB-T dongle
- 1x MicroSD card
- https://github.com/antirez/dump1090
- https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
- https://github.com/wiedehopf/tar1090
Project status (2024-11-22)
So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb
(as a replacement for dump1090
), tar1090
, tar1090-db
and mlat-client
(not used yet).
Current status:
- Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
- Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
- Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
- Local web interface (tar1090) to easily visualize the results
- Cockpit pre-configured to ease maintenance
What's missing:
- MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
- FlightAware support
Give it a go at https://g7.github.io/adsbreceiver/ !
Project links
- https://g7.github.io/adsbreceiver/
- https://github.com/g7/adsbreceiver
- https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver
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
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.
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
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.
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.
Gen-AI chatbots and test-automation of generated responses by mdati
Description
Start experimenting the generative SUSE-AI chat bot, asking questions on different areas of knowledge or science and possibly analyze the quality of the LLM model response, specific and comparative, checking the answers provided by different LLM models to a same query, using proper quality metrics or tools or methodologies.
Try to define basic guidelines and requirements for quality test automation of AI-generated responses.
First approach of investigation can be based on manual testing: methodologies, findings and data can be useful then to organize valid automated testing.
Goals
- Identify criteria and measuring scales for assessment of a text content.
- Define quality of an answer/text based on defined criteria .
- Identify some knowledge sectors and a proper list of problems/questions per sector.
- Manually run query session and apply evaluation criteria to answers.
- Draft requirements for test automation of AI answers.
Resources
- Announcement of SUSE-AI for Hack Week in Slack
- Openplatform and related 3 LLM models gemma:2b, llama3.1:8b, qwen2.5-coder:3b.
Notes
Foundation models (FMs):
are large deep learning neural networks, trained on massive datasets, that have changed the way data scientists approach machine learning (ML). Rather than develop artificial intelligence (AI) from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively.Large language models (LLMs):
are a category of foundation models pre-trained on immense amounts of data acquiring abilities by learning statistical relationships from vast amounts of text during a self- and semi-supervised training process, making them capable of understanding and generating natural language and other types of content , to perform a wide range of tasks.
LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language.
Validation of a AI-generated answer is not an easy task to perform, as manually as automated.
An LLM answer text shall contain a given level of informations: correcness, completeness, reasoning description etc.
We shall rely in properly applicable and measurable criteria of validation to get an assessment in a limited amount of time and 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)