New migration tool for Leap by lkocman

Update

I will call a meeting with other interested people at 11:00 CET https://meet.opensuse.org/migrationtool

Description

SLES 16 plans to have no yast tool in it. Leap 16 might keep some bits, however, we need a new tool for Leap to SLES migration, as this was previously handled by a yast2-migration-sle

Goals

A tool able to migrate Leap 16 to SLES 16, I would like to cover also other scenarios within openSUSE, as in many cases users would have to edit repository files manually.

• Leap -> Leap n+1 (minor and major version updates)
• Leap -> SLES docs
• Leap -> Tumbleweed
• Leap -> Slowroll
• Leap Micro -> Leap Micro n+1 (minor and major version updates)
• Leap Micro -> MicroOS

Hackweek 24 update

Marcela and I were working on the project from Brno coworking as well as finalizing pieces after the hackweek. We've tested several migration scenarios and it works. But it needs further polishing and testing.

Projected was renamed to opensuse-migration-tool and was submitted to devel project https://build.opensuse.org/requests/1227281

Repository

https://github.com/openSUSE/opensuse-migration-tool

Out of scope is any migration to an immutable system. I know Richard already has some tool for that.

Resources

Tracker for yast stack reduction code-o-o/leap/features#173 YaST stack reduction

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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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