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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ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

Overview

• Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
• Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
• Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
• Extensibility: Easily extend functionality with custom plugins and configurations.
• Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
• Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

Features

• distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
  
• Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
• Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
• Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
• Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
• One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
• Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

Planned features (Wishlist / TODOs)

• Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

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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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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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Integrate Backstage with Rancher Manager by nwmacd

Description

Backstage (backstage.io) is an open-source, CNCF project that allows you to create your own developer portal. There are many plugins for Backstage.

This could be a great compliment to Rancher Manager.

Goals

Learn and experiment with Backstage and look at how this could be integrated with Rancher Manager. Goal is to have some kind of integration completed in this Hack week.

Progress

Screen shot of home page at the end of Hackweek:

Day One

• Got Backstage running locally, understanding configuration with HTTPs.
• Got Backstage embedded in an IFRAME inside of Rancher
• Added content into the software catalog (see: https://backstage.io/docs/features/techdocs/getting-started/)
• Understood more about the entity model

Day Two

• Connected Backstage to the Rancher local cluster and configured the Kubernetes plugin.
• Created Rancher theme to make the light theme more consistent with Rancher

Days Three and Day Four

• Created two backend plugins for Backstage:

  1. Catalog Entity Provider - this imports users from Rancher into Backstage
  2. Auth Provider - uses the proxied sign-in pattern to check the Rancher session cookie, to user that to authenticate the user with Rancher and then log them into Backstage by connecting this to the imported User entity from the catalog entity provider plugin.

• With this in place, you can single-sign-on between Rancher and Backstage when it is deployed within Rancher. Note this is only when running locally for development at present

Day Five

• Start to build out a production deployment for all of the above
• Made some progress, but hit issues with the authentication and proxying when running proxied within Rancher, which needs further investigation

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Dartboard TUI by IValentin

Description

Our scalability and performance testing swiss-army knife tool Dartboard is a major WIP so why not add more scope creep? Dartboard is a cli tool which enables users to:

• Define a "Dart" config file as YAML which defines the various components to be created/setup when Dartboard runs its commands
• Spin up infrastructure utilizing opentofu/terraform providers
• Setup K3s or RKE2 clusters on the newly created infrastructure
• Deploy Rancher (with or without downstream cluster), rancher-monitoring (Grafana + Prometheus)
• Create resources in-bulk within the newly created Rancher cluster (ConfigMaps, Secrets, Users, Roles, etc.)
• Run various performance and scalability tests via k6
• Export/Import various tracked metrics (WIP)

Given all these features (and the features to come), it can be difficult to onboard and transfer knowledge of the tool. With a TUI, Dartboard's usage complexity can be greatly reduced!

Goals

• Create a TUI for Dartboard's "subcommands"
• Gain more familiarity with Dartboard and create a more user-friendly interface to enable others to use it
• Stretch Create a TUI workflow for generating a Dart file

Resources

https://github.com/charmbracelet/bubbletea

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Learn enough Golang and hack on CoreDNS by jkuzilek

Description

I'm implementing a split-horizon DNS for my home Kubernetes cluster to be able to access my internal (and external) services over the local network through public domains. I managed to make a PoC with the k8s_gateway plugin for CoreDNS. However, I soon found out it responds with IPs for all Gateways assigned to HTTPRoutes, publishing public IPs as well as the internal Loadbalancer ones.

To remedy this issue, a simple filtering mechanism has to be implemented.

Goals

• Learn an acceptable amount of Golang
• Implement GatewayClass (and IngressClass) filtering for k8s_gateway
• Deploy on homelab cluster
• Profit?

Resources

• https://github.com/ori-edge/k8s_gateway/issues/36
• https://github.com/coredns/coredns/issues/2465#issuecomment-593910983

EDIT: Feature mostly complete. An unfinished PR lies here. Successfully tested working on homelab cluster.

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WebUI for your data by avicenzi

A single place to view every bit of data you have.

Problem

You have too much data and you are a data hoarder.

• Family photos and videos.
• Lots of eBooks, TV Shows, Movies, and else.
• Boxes full of papers (taxes, invoices, IDs, certificates, exams, and else).
• Bank account statements (multiple currencies, countries, and people).

Maybe you have some data on S3, some on your NAS, and some on your local PC.

• How do you get it all together?
• How do you link a bank transaction to a product invoice?
• How to tag any object type and create a collection out of it (mix videos, photos, PDFs, transactions)?
• How to store this? file/folder structure does not work, everything is linked together

Project Description

The idea is a place where you can throw all your data, photos, videos, documents, binaries, and else.

Create photo albums, document collections, add tags across multiple file-formats, link content, and else.

The UI should be easy to use, where the data is not important for now (could be all S3 or local drive).

Similar proposals

The closest I found so far is https://perkeep.org/, but this is not what I'm looking for.

Goal for this Hackweek

Create a web UI, in Svelte ideally, perhaps React.

It should be able to show photos and videos at least.

Resources

None so far, this is just an idea.

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ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

Overview

• Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
• Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
• Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
• Extensibility: Easily extend functionality with custom plugins and configurations.
• Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
• Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

Features

• distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
  
• Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
• Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
• Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
• Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
• One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
• Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

Planned features (Wishlist / TODOs)

• Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

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terraform-provider-feilong by e_bischoff

Project Description

People need to test operating systems and applications on s390 platform.

Installation from scratch solutions include:

• just deploy and provision manually (with the help of ftpboot script, if you are at SUSE)
• use s3270 terminal emulation (used by openQA people?)
• use LXC from IBM to start CP commands and analyze the results
• use zPXE to do some PXE-alike booting (used by the orthos team?)
• use tessia to install from scratch using autoyast
• use libvirt for s390 to do some nested virtualization on some already deployed z/VM system
• directly install a Linux kernel on a LPAR and use kvm + libvirt from there

Deployment from image solutions include:

• use ICIC web interface (openstack in disguise, contributed by IBM)
• use ICIC from the openstack terraform provider (used by Rancher QA)
• use zvm_ansible to control SMAPI
• connect directly to SMAPI low-level socket interface

IBM Cloud Infrastructure Center (ICIC) harnesses the Feilong API, but you can use Feilong without installing ICIC, provided you set up a "z/VM cloud connector" into one of your VMs following this schema.

What about writing a terraform Feilong provider, just like we have the terraform libvirt provider? That would allow to transparently call Feilong from your main.tf files to deploy and destroy resources on your system/z.

Other Feilong-based solutions include:

• make libvirt Feilong-aware
• simply call Feilong from shell scripts with curl
• use zvmconnector client python library from Feilong
• use zthin part of Feilong to directly command SMAPI.

Goal for Hackweek 23

My final goal is to be able to easily deploy and provision VMs automatically on a z/VM system, in a way that people might enjoy even outside of SUSE.

My technical preference is to write a terraform provider plugin, as it is the approach that involves the least software components for our deployments, while remaining clean, and compatible with our existing development infrastructure.

Goals for Hackweek 24

Feilong provider works and is used internally by SUSE Manager team. Let's push it forward!

Let's add support for fiberchannel disks and multipath.

Goals for Hackweek 25

• Finish support for fiberchannel disks and multipath
• Fix problems with registration on hashicorp providers registry

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Cobbler Angular Web Interface by SchoolGuy

Project Description

The old Cobbler webinterface was built into the server, leading to a huge dependency stack only required for a few people.

Goal for this Hackweek

The project should aim to finalize the first prototype of the new Angular based web interface.

A secondary goal of this hackweek is to learn a lot of Angular.

Update for Hackweek 24

The GH project received some traction since I have some vacation. As such it is my aim to get a first alpha released to close the milestone 0.0.1 (or whatever version I can release with semantic release).

Resources

• GitHub Repo
• GitHub Project

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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!

---

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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