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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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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Ansible for add-on management by lmanfredi

Description

Machines can contains various combinations of add-ons and are often modified during the time.

The list of repos can change so I would like to create an automation able to reset the status to a given state, based on metadata available for these machines

Goals

Create an Ansible automation able to take care of add-on (repo list) configuration using metadata as reference

Resources

• Machines
• Repositories
• Developing modules
• Basic VM Guest management
• Module zypper_repository_list
• ansible-collections community.general
  • plugins/modules/zypper.py
  • plugins/modules/zypper_repository.py
  • plugins/modules/zypperrepositoryinfo.py

Results

Created WIP project Ansible-add-on-openSUSE

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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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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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SUSE KVM Best Practices by roseswe

Description

SUSE Best Practices around KVM, especially for SAP workloads. Early Google presentation already made from various customer projects and SUSE sources.

Goals

Complete presentation we can reuse in SUSE Consulting projects

Resources

KVM (virt-manager) images

SUSE/SAP/KVM Best Practices

• https://documentation.suse.com/en-us/sles/15-SP6/single-html/SLES-virtualization/
• SAP Note 1522993 - "Linux: SAP on SUSE KVM - Kernel-based Virtual Machine" && 2284516 - SAP HANA virtualized on SUSE Linux Enterprise hypervisors https://me.sap.com/notes/2284516
• SUSECon24: [TUTORIAL-1253] Virtualizing SAP workloads with SUSE KVM || https://youtu.be/PTkpRVpX2PM
• SUSE Best Practices for SAP HANA on KVM - https://documentation.suse.com/sbp/sap-15/html/SBP-SLES4SAP-HANAonKVM-SLES15SP4/index.html

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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:
  • deepseek-coder-v2
  • dolphin-mistral
  • starcoder2
  • (...others??)
    
• Setup a Web UI for it, choosing an easily extensible and customizable option between:
  • openwebui
  • localAI
  • lobachat
  • (...others??)
    
• 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

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