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)

---

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):
  • 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

---

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.

---

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/

---

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.

---

FamilyTrip Planner: A Personalized Travel Planning Platform for Families by pherranz

Description

FamilyTrip Planner is an innovative travel planning application designed to optimize travel experiences for families with children. By integrating APIs for flights, accommodations, and local activities, the app generates complete itineraries tailored to each family’s unique interests and needs. Recommendations are based on customizable parameters such as destination, trip duration, children’s ages, and personal preferences. FamilyTrip Planner not only simplifies the travel planning process but also offers a comprehensive, personalized experience for families.

Goals

This project aims to: - Create a user-friendly platform that assists families in planning complete trips, from flight and accommodation options to recommended family-friendly activities. - Provide intelligent, personalized travel itineraries using artificial intelligence to enhance travel enjoyment and minimize time and cost. - Serve as an educational project for exploring Go programming and artificial intelligence, with the goal of building proficiency in both.

Resources

To develop FamilyTrip Planner, the project will leverage: - APIs such as Skyscanner, Google Places, and TripAdvisor to source real-time information on flights, accommodations, and activities. - Go programming language to manage data integration, API connections, and backend development. - Basic machine learning libraries to implement AI-driven itinerary suggestions tailored to family needs and preferences.

---

Small healthcheck tool for Longhorn by mbrookhuis

Project Description

We have often problems (e.g. pods not starting) that are related to PVCs not running, cluster (nodes) not all up or deployments not running or completely running. This all prevents administration activities. Having something that can regular be run to validate the status of the cluster would be helpful, and not as of today do a lot of manual tasks.

As addition (read enough time), we could add changing reservation, adding new disks, etc. --> This didn't made it. But the scripts can easily be adopted.

This tool would decrease troubleshooting time, giving admins rights to the rancher GUI and could be used in automation.

Goal for this Hackweek

At the end we should have a small python tool that is doing a (very) basic health check on nodes, deployments and PVCs. First attempt was to make it in golang, but that was taking to much time.

Overview

This tool will run a simple healthcheck on a kubernetes cluster. It will perform the following actions:

• node check: This will check all nodes, and display the status and the k3s version. If the status of the nodes is not "Ready" (this should be only reported), the cluster will be reported as having problems

• deployment check: This check will list all deployments, and display the number of expected replicas and the used replica. If there are unused replicas this will be displayed. The cluster will be reported as having problems.

• pvc check: This check will list of all pvc's, and display the status and the robustness. If the robustness is not "Healthy", the cluster will be reported as having problems.

If there is a problem registered in the checks, there will be a warning that the cluster is not healthy and the program will exit with 1.

The script has 1 mandatory parameter and that is the kubeconf of the cluster or of a node off the cluster.

The code is writen for Python 3.11, but will also work on 3.6 (the default with SLES15.x). There is a venv present that will contain all needed packages. Also, the script can be run on the cluster itself or any other linux server.

Installation

To install this project, perform the following steps:

• Create the directory /opt/k8s-check

mkdir /opt/k8s-check

• Copy all the file to this directory and make the following changes:

chmod +x k8s-check.py

---

kubectl clone: Seamlessly Clone Kubernetes Resources Across Multiple Rancher Clusters and Projects by dpunia

Description

kubectl clone is a kubectl plugin that empowers users to clone Kubernetes resources across multiple clusters and projects managed by Rancher. It simplifies the process of duplicating resources from one cluster to another or within different namespaces and projects, with optional on-the-fly modifications. This tool enhances multi-cluster resource management, making it invaluable for environments where Rancher orchestrates numerous Kubernetes clusters.

Goals

1. Seamless Multi-Cluster Cloning
   • Clone Kubernetes resources across clusters/projects with one command.
   • Simplifies management, reduces operational effort.

Resources

1. Rancher & Kubernetes Docs

   • Rancher API, Cluster Management, Kubernetes client libraries.

2. Development Tools

   • Kubectl plugin docs, Go programming resources.

Building and Installing the Plugin

1. Set Environment Variables: Export the Rancher URL and API token:

• export RANCHER_URL="https://rancher.example.com"
• export RANCHER_TOKEN="token-xxxxx:xxxxxxxxxxxxxxxxxxxx"

1. Build the Plugin: Compile the Go program:

• go build -o kubectl-clone ./pkg/

1. Install the Plugin: Move the executable to a directory in your PATH:

• mv kubectl-clone /usr/local/bin/

Ensure the file is executable:

• chmod +x /usr/local/bin/kubectl-clone

1. Verify the Plugin Installation: Test the plugin by running:

• kubectl clone --help

You should see the usage information for the kubectl-clone plugin.

Usage Examples

1. Clone a Deployment from One Cluster to Another:

• kubectl clone --source-cluster c-abc123 --type deployment --name nginx-deployment --target-cluster c-def456 --new-name nginx-deployment-clone

1. Clone a Service into Another Namespace and Modify Labels:

---

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.

---

Harvester Packer Plugin by mrohrich

Description

Hashicorp Packer is an automation tool that allows automatic customized VM image builds - assuming the user has a virtualization tool at their disposal. To make use of Harvester as such a virtualization tool a plugin for Packer needs to be written. With this plugin users could make use of their Harvester cluster to build customized VM images, something they likely want to do if they have a Harvester cluster.

Goals

Write a Packer plugin bridging the gap between Harvester and Packer. Users should be able to create customized VM images using Packer and Harvester with no need to utilize another virtualization platform.

Resources

Hashicorp documentation for building custom plugins for Packer https://developer.hashicorp.com/packer/docs/plugins/creation/custom-builders

Source repository of the Harvester Packer plugin https://github.com/m-ildefons/harvester-packer-plugin

---

Learn enough Golang and hack on CoreDNS by jkuzilek

Description

I'm implementing a split-horizon DNS for my home Kubernetes cluster to be able to access my internal (and external) services over the local network through public domains. I managed to make a PoC with the k8s_gateway plugin for CoreDNS. However, I soon found out it responds with IPs for all Gateways assigned to HTTPRoutes, publishing public IPs as well as the internal Loadbalancer ones.

To remedy this issue, a simple filtering mechanism has to be implemented.

Goals

• Learn an acceptable amount of Golang
• Implement GatewayClass (and IngressClass) filtering for k8s_gateway
• Deploy on homelab cluster
• Profit?

Resources

• https://github.com/ori-edge/k8s_gateway/issues/36
• https://github.com/coredns/coredns/issues/2465#issuecomment-593910983

EDIT: Feature mostly complete. An unfinished PR lies here. Successfully tested working on homelab cluster.

---

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

---

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

---

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.

---