iSCSI integration in Warewulf by ncuralli

Description

This Hackweek project aims to enhance Warewulf’s capabilities by adding iSCSI support, enabling both remote boot and flexible mounting of iSCSI devices within the filesystem. The project, which already handles NFS, DHCP, and iPXE, will be extended to offer iSCSI services as well, centralizing all necessary services for provisioning and booting cluster nodes.

Goals

• iSCSI Boot Option: Enable nodes to boot directly from iSCSI volumes
• Mounting iSCSI Volumes within the Filesystem: Implement support for mounting iSCSI devices at various points within the filesystem

Resources

https://warewulf.org/

Steps

• add generic framework to handle remote ressource/filesystems to wwctl [ ]
• add iSCSI handling to wwctl configure [ ]
• add iSCSI to dracut files [ ]
• test it [ ]

Mortgage Plan Analyzer by RMestre

https://github.com/rjpmestre/mortgage-plan-analyzer

Project Description

Many people face challenges when trying to renegotiate their mortgages with different banks. They receive offers from multiple lenders and struggle to compare them effectively. Each proposal may have slightly different terms and data presentation, making it hard to make informed decisions. Additionally, understanding the impact of various taxes and variables can be complex. The Mortgage Plan Analyzer project aims to address these issues.

Project Overview:

The Mortgage Plan Analyzer is a web-based tool built using PHP, Laravel, Livewire, and AdminLTE/bootstrap. It provides a user-friendly platform for individuals to input basic specifications about their mortgage, adjust taxes and variables, and obtain short-term projections for each proposal. Users can also compare multiple mortgage offers side by side, enabling them to make informed decisions about their mortgage renegotiation.

Why Start This Project:

I found myself in this position and most tools I found around are either for marketing/selling purposes or not flexible enough. As i was starting getting lost in a jungle of spreadsheets i thought I could just create a tool to help me and others that may be experiencing the same struggles to provide clarity and transparency in the decision-making process.

Hackweek 25 ideas (to refine still :) )

• Euribor Trends in Projections
• - Use historical Euribor data to model optimistic and pessimistic scenarios for variable-rate loans.
• Use the annual summaries (installments, amortizations, etc) and run some analysis to highlight key differences, like short-term savings vs. long-term costs
• Financial plan can be hard/boring to follow. Create a simple viewing mode that summarizes monthly values and their annual sums.

Hackweek 24 update

• Improved summaries graphs by adding:
• - Line graph;
• - Accumulated line graph;
• - Set the range to short/mid/long term;
• - Highlight best simulation and value per year;
• Improve the general behaviour of the forms:
• - Simulations name setting;
• - Cloning simulations;
• - Adjust update timing on input changes;
• Show/Hide big tables;
• Support multi languages (added english);
• Added examples;
• Adjustments to fonts and sizes;
• Fixed loading screen;
• Dependencies adjustments;

Hackweek 23 initial release

• Developed a base site that:
• - Allows adding up to 3 simulations;
• - Create financial plans;
• - Simulations comparison graph for the first 4 years;
• Created Github project @ https://github.com/rjpmestre/mortgage-plan-analyzer ;
• Launched a demo instance using Oracle Cloud Free Tier currently @ http://138.3.251.182/

Resources

• Banco de Portugal: Main simulator all portuguese banks have to follow ( https://clientebancario.bportugal.pt/credito-habitacao )
• Laravel: A PHP web application framework for building robust and scalable applications. ( https://laravel.com/ )
• Livewire: A Laravel library for building dynamic interfaces without writing JavaScript. ( https://livewire.laravel.com/ )
• AdminLTE: A responsive admin dashboard template for creating a visually appealing interface. ( https://adminlte.io/ )

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

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