Learn how to integrate Elixir and Phoenix Liveview with LLMs by ninopaparo

Description

Learn how to integrate Elixir and Phoenix Liveview with LLMs by building an application that can provide answers to user queries based on a corpus of custom-trained data.

Goals

Develop an Elixir application via the Phoenix framework that:

• Employs Retrieval Augmented Generation (RAG) techniques
• Supports the integration and utilization of various Large Language Models (LLMs).
• Is designed with extensibility and adaptability in mind to accommodate future enhancements and modifications.

Resources

• https://elixir-lang.org/
• https://www.phoenixframework.org/
• https://github.com/elixir-nx/bumblebee
• https://ollama.com/

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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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Install Uyuni on Kubernetes in cloud-native way by cbosdonnat

Description

For now installing Uyuni on Kubernetes requires running mgradm on a cluster node... which is not what users would do in the Kubernetes world. The idea is to implement an installation based only on helm charts and probably an operator.

Goals

Install Uyuni from Rancher UI.

Resources

• mgradm code: https://github.com/uyuni-project/uyuni-tools
• Uyuni operator: https://github.com/cbosdo/uyuni-operator

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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:

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Introducing "Bottles": A Proof of Concept for Multi-Version CRD Management in Kubernetes by aruiz

Description

As we delve deeper into the complexities of managing multiple CRD versions within a single Kubernetes cluster, I want to introduce "Bottles" - a proof of concept that aims to address these challenges.

Bottles propose a novel approach to isolating and deploying different CRD versions in a self-contained environment. This would allow for greater flexibility and efficiency in managing diverse workloads.

Goals

• Evaluate Feasibility: determine if this approach is technically viable, as well as identifying possible obstacles and limitations.
• Reuse existing technology: leverage existing products whenever possible, e.g. build on top of Kubewarden as admission controller.
• Focus on Rancher's use case: the ultimate goal is to be able to use this approach to solve Rancher users' needs.

Resources

Core concepts:

• ConfigMaps: Bottles could be defined and configured using ConfigMaps.
• Admission Controller: An admission controller will detect "bootled" CRDs being installed and replace the resource name used to store them.
• Aggregated API Server: By analyzing the author of a request, the aggregated API server will determine the correct bottle and route the request accordingly, making it transparent for the user.

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Mammuthus - The NFS-Ganesha inside Kubernetes controller by vcheng

Description

As the user-space NFS provider, the NFS-Ganesha is wieldy use with serval projects. e.g. Longhorn/Rook. We want to create the Kubernetes Controller to make configuring NFS-Ganesha easy. This controller will let users configure NFS-Ganesha through different backends like VFS/CephFS.

Goals

1. Create NFS-Ganesha Package on OBS: nfs-ganesha5, nfs-ganesha6
2. Create NFS-Ganesha Container Image on OBS: Image
3. Create a Kubernetes controller for NFS-Ganesha and support the VFS configuration on demand. Mammuthus

Resources

NFS-Ganesha

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