Create object oriented API for perl's YAML::XS module, with YAML 1.2 Support by tinita

Description

YAML::XS is a binding to libyaml and already quite old, but the most popular YAML module for perl. There are two main issues:

• It uses global package variables to influence behaviour.
• It didn't implement the loading of types like numbers and booleans according to the YAML spec (neither 1.1 nor 1.2).

Goals

Create a new interface which works object oriented. Currently YAML::XS exports a list of functions.

• The new API will allow to create a YAML::XS object containing configuration influencing the behaviour of loading and dumping.
  • It keeps the libyaml parser and emitter structs in memory, so repeated calls can save the creation of those structs
• It will by default implement the YAML 1.2 Core Schema, so it is compatible to other YAML processors in perl and in other languages
• If I have time, I would like to add the merge << key feature as an option. We could then use it in openQA as a replacement for YAML::PP to be faster.

I already created a proof of concept with a minimal functionality some weeks before this HackWeek.

Resources

• Work is currently happening on the oop branch
• Experimental release waiting for user feedback: https://github.com/perlpunk/yaml-libyaml-pm/releases
• Diff

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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.

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Saline (state deployment control and monitoring tool for SUSE Manager/Uyuni) by vizhestkov

Project Description

Saline is an addition for salt used in SUSE Manager/Uyuni aimed to provide better control and visibility for states deploymend in the large scale environments.

In current state the published version can be used only as a Prometheus exporter and missing some of the key features implemented in PoC (not published). Now it can provide metrics related to salt events and state apply process on the minions. But there is no control on this process implemented yet.

Continue with implementation of the missing features and improve the existing implementation:

• authentication (need to decide how it should be/or not related to salt auth)

• web service providing the control of states deployment

Goal for this Hackweek

• Implement missing key features

• Implement the tool for state deployment control with CLI

Resources

https://github.com/openSUSE/saline

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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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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/

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Team Hedgehogs' Data Observability Dashboard by gsamardzhiev

Description

This project aims to develop a comprehensive Data Observability Dashboard that provides r insights into key aspects of data quality and reliability. The dashboard will track:

Data Freshness: Monitor when data was last updated and flag potential delays.

Data Volume: Track table row counts to detect unexpected surges or drops in data.

Data Distribution: Analyze data for null values, outliers, and anomalies to ensure accuracy.

Data Schema: Track schema changes over time to prevent breaking changes.

The dashboard's aim is to support historical tracking to support proactive data management and enhance data trust across the data function.

Goals

Although the final goal is to create a power bi dashboard that we are able to monitor, our goals is to 1. Create the necessary tables that track the relevant metadata about our current data 2. Automate the process so it runs in a timely manner

Resources

AWS Redshift; AWS Glue, Airflow, Python, SQL

Why Hedgehogs?

Because we like them.

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Automation of ABI compatibility checks by ateixeira

Description

ABI compatibility checks could be further automated by using the OBS API to download built RPMs and using existing tools to analyze ABI compatibility between the libraries contained in those packages. This project aims to explore these possibilities and figure out a way to make ABI checks as painless and fast as possible for package maintainers.

Resources

https://github.com/openSUSE/abi-compliance-checker

https://github.com/lvc/abi-compliance-checker

https://sourceware.org/libabigail/

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Learn about OSB and contribute to `kustomize` and `k9s` packages to add ARM arch by dpock

Description

There are already k9s and kustomize packages that exist for openSUSE today. These could be used as the source for these binaries in our rancher projects. By using them we would benefit from CVE fixes included in our distribution of the packages not in cluded upstream. However they are not providing arm package builds which are required.

Goals

• [ ] Update the kustomize package in OBS to use the newest version and send change request

Resources

• k9s: https://build.opensuse.org/package/show/openSUSE:Factory/k9s
• kustomize: https://build.opensuse.org/package/show/openSUSE:Factory/kustomize
• Learning Docs: https://confluence.suse.com/display/packaging/Training%2C+Talks+and+Videos

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New features in openqa-trigger-from-obs for openQA by jlausuch

Description

Implement new features in openqa-trigger-from-obs to make xml more flexible.

Goals

One of the features to be implemented: - Possibility to define "VERSION" and "ARCH" variables per flavor instead of global.

Resources

https://github.com/os-autoinst/openqa-trigger-from-obs

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Git CI to automate the creation of product definition by gyribeiro

Description

Automate the creation of product definition

Goals

Create a Git CI that will:

• automatically be triggered once a change (commit) in package list is done.
• run tool responsible to update product definition based on the changes in package list
• test the updated product definition in OBS
• submit a pull request updating the product definition in the repository

NOTE: this Git CI may also be triggered manually

Resources

• https://docs.gitlab.com/ee/ci/
• https://openbuildservice.org/2021/05/31/scm-integration/
• https://github.com/openSUSE/openSUSE-release-tools

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obs-service-vendor_node_modules by cdimonaco

Description

When building a javascript package for obs, one option is to use https://github.com/openSUSE/obs-service-node_modules as source service to get the project npm dependencies available for package bulding.

obs-service-vendornodemodules aims to be a source service that vendors npm dependencies, installing them with npm install (optionally only production ones) and then creating a tar package of the installed dependencies.

The tar will be used as source in the package building definitions.

Goals

• Create an obs service package that vendors the npm dependencies as tar archive.
• Maybe add some macros to unpack the vendor package in the specfiles

Resources

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