Project Description

We know that Kubernetes clusters need control planes but running them in dedicated VMs might not be always efficient, instead, we can run them as pods within the management cluster. This project aims to solve the following problems:

Reduce the cost of provisioning control planes
Provide a declarative API for control plane management
Centralize management of control planes and decouple them from workers
Provide k3s clusters as a service on any infrastructure.

Goal for this Hackweek

Look into existing solutions like https://github.com/clastix/kamaji
Figure out if any can be reused for managing K3S
Investigate what changes need to be done in K3S in order to support this use-case
Build an API that allows provisioning and configuring of K3S control planes, see https://github.com/zawachte/cluster-api-k3s/ for generating k3s configuration using K8S API.

Resources

No project repository for now, all hacking will be done in these forks: https://github.com/alexander-demicev/kamaji https://github.com/alexander-demicev/k3s

What was achieved during hack week?

I was able to deploy k3s in a pod using experimental agentless feature https://docs.k3s.io/advanced#running-agentless-servers-experimental, meaning the server will not run kubelet, container runtime, or CNI
It was possible to deploy an external etcd and connect agentless servers to it https://docs.k3s.io/installation/ha#2-launch-server-nodes
I managed to run and connect a worker node to control planes running in pods

What wasn't done during hack week?

After some investigation I came to conclusion that kamaji might be reused but with some changes to its codebase as we are plugging our kubernetes distro
I was working on POC operator based on CAPI k3s provider https://github.com/zawachte/cluster-api-k3s/, the operator would manage pod deployments instead of CAPI machines but one week is not enough to get it working

Join this project Leave this project

Looking for hackers with the skills:

rancher containers kubernetes edge k3s go

This project is part of:

Hack Week 22

Activity

almost 2 years ago: flonnegren liked this project.

almost 2 years ago: fgiudici liked this project.

almost 2 years ago: paulgonin liked this project.

almost 2 years ago: ademicev0 started this project.

almost 2 years ago: ademicev0 added keyword "rancher" to this project.

almost 2 years ago: ademicev0 added keyword "containers" to this project.

almost 2 years ago: ademicev0 added keyword "kubernetes" to this project.

almost 2 years ago: ademicev0 added keyword "edge" to this project.

almost 2 years ago: ademicev0 added keyword "k3s" to this project.

almost 2 years ago: ademicev0 added keyword "go" to this project.

almost 2 years ago: ademicev0 originated this project.

Comments

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rancher

Cluster API Provider for Harvester by rcase

Project Description

The Cluster API "infrastructure provider" for Harvester, also named CAPHV, makes it possible to use Harvester with Cluster API. This enables people and organisations to create Kubernetes clusters running on VMs created by Harvester using a declarative spec.

The project has been bootstrapped in HackWeek 23, and its code is available here.

Work done in HackWeek 2023

Have a early working version of the provider available on Rancher Sandbox : *DONE *
Demonstrated the created cluster can be imported using Rancher Turtles: DONE
Stretch goal - demonstrate using the new provider with CAPRKE2: DONE and the templates are available on the repo

Goals for HackWeek 2024

Add support for ClusterClass
Add e2e testing
Add more Unit Tests
Improve Status Conditions to reflect current state of Infrastructure
Improve CI (some bugs for release creation)
Testing with newer Harvester version (v1.3.X and v1.4.X)
Due to the length and complexity of the templates, maybe package some of them as Helm Charts.
Other improvement suggestions are welcome!

DONE in HackWeek 24:

Add more Unit Tests
Improve Status Conditions for some phases
Add cloud provider config generation
Testing with Harvester v1.3.2
Template improvements
Issues creation

Thanks to @isim and Dominic Giebert for their contributions!

Resources

Looking for help from anyone interested in Cluster API (CAPI) or who wants to learn more about Harvester.

This will be an infrastructure provider for Cluster API. Some background reading for the CAPI aspect:

Rancher microfrontend extensions by ftorchia

Description

Rancher UI Extensions allow users, developers, partners, and customers to extend and enhance the Rancher UI. Extensions are Helm charts that can only be installed once into a cluster. The charts contain a UI built package that is downloaded and linked to the Host UI at runtime; this means that the extension pkg needs to be implemented using the same technology and have the same APIs as Rancher UI.

Goals

We want to create a new type of Rancher extension, based on microfrontend pattern. The extension is served in a docker container in the k8s clusters and embedded in the host UI; this would guarantee us to be able to create extensions unrelated to the rancher UI architecture, in any technology.

Non Goals

We want to apply the microfrontend pattern to the product-level extensions; we don't want to apply it to cluster-level extensions.

Resources

rancher-extension-microfrontend, Rancher extensions

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.

Small healthcheck tool for Longhorn by mbrookhuis

Project Description

We have often problems (e.g. pods not starting) that are related to PVCs not running, cluster (nodes) not all up or deployments not running or completely running. This all prevents administration activities. Having something that can regular be run to validate the status of the cluster would be helpful, and not as of today do a lot of manual tasks.

As addition (read enough time), we could add changing reservation, adding new disks, etc. --> This didn't made it. But the scripts can easily be adopted.

This tool would decrease troubleshooting time, giving admins rights to the rancher GUI and could be used in automation.

Goal for this Hackweek

At the end we should have a small python tool that is doing a (very) basic health check on nodes, deployments and PVCs. First attempt was to make it in golang, but that was taking to much time.

Overview

This tool will run a simple healthcheck on a kubernetes cluster. It will perform the following actions:

node check: This will check all nodes, and display the status and the k3s version. If the status of the nodes is not "Ready" (this should be only reported), the cluster will be reported as having problems
deployment check: This check will list all deployments, and display the number of expected replicas and the used replica. If there are unused replicas this will be displayed. The cluster will be reported as having problems.
pvc check: This check will list of all pvc's, and display the status and the robustness. If the robustness is not "Healthy", the cluster will be reported as having problems.

If there is a problem registered in the checks, there will be a warning that the cluster is not healthy and the program will exit with 1.

The script has 1 mandatory parameter and that is the kubeconf of the cluster or of a node off the cluster.

The code is writen for Python 3.11, but will also work on 3.6 (the default with SLES15.x). There is a venv present that will contain all needed packages. Also, the script can be run on the cluster itself or any other linux server.

Installation

To install this project, perform the following steps:

Create the directory /opt/k8s-check

mkdir /opt/k8s-check

Copy all the file to this directory and make the following changes:

chmod +x k8s-check.py

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

containers

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

Enable the containerized Uyuni server to run on different host OS by j_renner

Description

The Uyuni server is provided as a container, but we still require it to run on Leap Micro? This is not how people expect to use containerized applications, so it would be great if we tested other host OSs and enabled them by providing builds of necessary tools for (e.g. mgradm). Interesting candidates should be:

openSUSE Leap
Cent OS 7
Ubuntu
???

Goals

Make it really easy for anyone to run the Uyuni containerized server on whatever OS they want (with support for containers of course).

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

Improve Development Environment on Uyuni by mbussolotto

Description

Currently create a dev environment on Uyuni might be complicated. The steps are:

add the correct repo
download packages
configure your IDE (checkstyle, format rules, sonarlint....)
setup debug environment
...

The current doc can be improved: some information are hard to be find out, some others are completely missing.

Dev Container might solve this situation.

Goals

Uyuni development in no time:

using VSCode:
- setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
- dev container should contains all dependencies
- setup debug environment
implement a GitHub Workspace solution
re-write documentation

Lots of pieces are already implemented: we need to connect them in a consistent solution.

Resources

https://github.com/uyuni-project/uyuni/wiki

ADS-B receiver with MicroOS by epaolantonio

I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time?

There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090) and web frontend (tar1090).

Goals

Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
Make it easy to install
Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)

Resources

1x Small Board Computer capable of running MicroOS
1x RTL2832U DVB-T dongle
1x MicroSD card
https://github.com/antirez/dump1090
https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
https://github.com/wiedehopf/tar1090

Project status (2024-11-22)

So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb (as a replacement for dump1090), tar1090, tar1090-db and mlat-client (not used yet).

Current status:

Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
Local web interface (tar1090) to easily visualize the results
Cockpit pre-configured to ease maintenance

What's missing:

MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
FlightAware support

Give it a go at https://g7.github.io/adsbreceiver/ !

Project links

kubernetes

ddflare: (Dynamic)DNS management via Cloudflare API in Kubernetes by fgiudici

Description

ddflare is a project started a couple of weeks ago to provide DDNS management using v4 Cloudflare APIs: Cloudflare offers management via APIs and access tokens, so it is possible to register a domain and implement a DynDNS client without any other external service but their API.

Since ddflare allows to set any IP to any domain name, one could manage multiple A and ALIAS domain records. Wouldn't be cool to allow full DNS control from the project and integrate it with your Kubernetes cluster?

Goals

Main goals are:

add containerized image for ddflare
extend ddflare to be able to add and remove DNS records (and not just update existing ones)
add documentation, covering also a sample pod deployment for Kubernetes
write a ddflare Kubernetes operator to enable domain management via Kubernetes resources (using kubebuilder)

Available tasks and improvements tracked on ddflare github.

Resources

https://github.com/fgiudici/ddflare
https://developers.cloudflare.com/api/
https://book.kubebuilder.io

Extending KubeVirtBMC's capability by adding Redfish support by zchang

Description

In Hack Week 23, we delivered a project called KubeBMC (renamed to KubeVirtBMC now), which brings the good old-fashioned IPMI ways to manage virtual machines running on KubeVirt-powered clusters. This opens the possibility of integrating existing bare-metal provisioning solutions like Tinkerbell with virtualized environments. We even received an inquiry about transferring the project to the KubeVirt organization. So, a proposal was filed, which was accepted by the KubeVirt community, and the project was renamed after that. We have many tasks on our to-do list. Some of them are administrative tasks; some are feature-related. One of the most requested features is Redfish support.

Goals

Extend the capability of KubeVirtBMC by adding Redfish support. Currently, the virtbmc component only exposes IPMI endpoints. We need to implement another simulator to expose Redfish endpoints, as we did with the IPMI module. We aim at a basic set of functionalities:

Power management
Boot device selection
Virtual media mount (this one is not so basic )

Resources

DMTF/Redfish-Interface-Emulator: The Redfish Interface Emulator can emulate a Redfish-based interface statically (GET) or dynamically (POST, PATCH, DELETE)

A CLI for Harvester by mohamed.belgaied

[comment]: # Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI [comment]: # Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. [comment]: # Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.

Project Description

Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as: harvester vm create my-vm --count 5 to create 5 VMs named my-vm-01 to my-vm-05.

Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.

Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli

Done in previous Hackweeks

Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE

Goal for this Hackweek

The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.

Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it

Issue list is here: https://github.com/belgaied2/harvester-cli/issues

Resources

The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact). Welcome contributions are:

Testing it and creating issues
Documentation
Go code improvement

What you might learn

Harvester CLI might be interesting to you if you want to learn more about:

GitHub Actions
Harvester as a SUSE Product
Go programming language
Kubernetes API

SUSE AI Meets the Game Board by moio

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

Small healthcheck tool for Longhorn by mbrookhuis

Project Description

As addition (read enough time), we could add changing reservation, adding new disks, etc. --> This didn't made it. But the scripts can easily be adopted.

This tool would decrease troubleshooting time, giving admins rights to the rancher GUI and could be used in automation.

Goal for this Hackweek

At the end we should have a small python tool that is doing a (very) basic health check on nodes, deployments and PVCs. First attempt was to make it in golang, but that was taking to much time.

Overview

This tool will run a simple healthcheck on a kubernetes cluster. It will perform the following actions:

node check: This will check all nodes, and display the status and the k3s version. If the status of the nodes is not "Ready" (this should be only reported), the cluster will be reported as having problems
deployment check: This check will list all deployments, and display the number of expected replicas and the used replica. If there are unused replicas this will be displayed. The cluster will be reported as having problems.
pvc check: This check will list of all pvc's, and display the status and the robustness. If the robustness is not "Healthy", the cluster will be reported as having problems.

If there is a problem registered in the checks, there will be a warning that the cluster is not healthy and the program will exit with 1.

The script has 1 mandatory parameter and that is the kubeconf of the cluster or of a node off the cluster.

Installation

To install this project, perform the following steps:

Create the directory /opt/k8s-check

mkdir /opt/k8s-check

Copy all the file to this directory and make the following changes:

chmod +x k8s-check.py

edge

Build Edge Image Builder ISO with SUSE Manager by mweiss2

Description

With SUSE Manager, we can build OS Images using KIWI and container images. As we have Edge Image Builder, we want to see if it is possible to use SUSE Manager to build/customize OS Images by integrating Edge Image Builder as well.

Goals

To make the process easier for customers, a single-build pipeline that automatically adds the combustion and artifact files from the EIB process is desirable.

Kiwi and EIB need to come from a Git Repository.
Kiwi and EIB need to be running as containers.
Configuration options for the images used for Kiwi and EIB build.
X86 and ARM64 Support.
SUSE Manager 4.3 and 5.X Support.
SLES 15 SP6 / SL Micro 6.0 and SL Micro 6.1 Support.

Outcome

Change the Kiwi build process to use Podman with the Kiwi image registry.suse.com/bci/kiwi:10.1.10
Change the Edge Image Builder to produce a combustion-only ISO
Extract the contents and write them to a dedicated /OEM partition integrated via Kiwi into the ISO Kiwi creates.

Sources and PRs

https://github.com/Martin-Weiss/kiwi-image-micro-gpu-60
https://github.com/suse-edge/edge-image-builder/pull/618
https://github.com/uyuni-project/uyuni/pull/9507

Edge Image Builder and mkosi for Uyuni by oholecek

Description

One part of Uyuni system management tool is ability to build custom images. Currently Uyuni supports only Kiwi image builder.

Kiwi however is not the only image building system out there and with the goal to also become familiar with other systems, this projects aim to add support for Edge Image builder and systemd's mkosi systems.

Goals

Uyuni is able to

provision EIB and mkosi build hosts
build EIB and mkosi images and store them

Resources

Uyuni - https://github.com/uyuni-project/uyuni
Edge Image builder - https://github.com/suse-edge/edge-image-builder
mkosi - https://github.com/systemd/mkosi

Small healthcheck tool for Longhorn by mbrookhuis

Project Description

As addition (read enough time), we could add changing reservation, adding new disks, etc. --> This didn't made it. But the scripts can easily be adopted.

This tool would decrease troubleshooting time, giving admins rights to the rancher GUI and could be used in automation.

Goal for this Hackweek

At the end we should have a small python tool that is doing a (very) basic health check on nodes, deployments and PVCs. First attempt was to make it in golang, but that was taking to much time.

Overview

This tool will run a simple healthcheck on a kubernetes cluster. It will perform the following actions:

node check: This will check all nodes, and display the status and the k3s version. If the status of the nodes is not "Ready" (this should be only reported), the cluster will be reported as having problems
deployment check: This check will list all deployments, and display the number of expected replicas and the used replica. If there are unused replicas this will be displayed. The cluster will be reported as having problems.
pvc check: This check will list of all pvc's, and display the status and the robustness. If the robustness is not "Healthy", the cluster will be reported as having problems.

If there is a problem registered in the checks, there will be a warning that the cluster is not healthy and the program will exit with 1.

The script has 1 mandatory parameter and that is the kubeconf of the cluster or of a node off the cluster.

Installation

To install this project, perform the following steps:

Create the directory /opt/k8s-check

mkdir /opt/k8s-check

Copy all the file to this directory and make the following changes:

chmod +x k8s-check.py

k3s

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

Description

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

go

Automate PR process by idplscalabrini

Description

This project is to streamline and enhance the pr review process by adding automation for identifying some issues like missing comments, identifying sensitive information in the PRs like credentials. etc. By leveraging GitHub Actions and golang hooks we can focus more on high-level reviews

Goals

Automate lints and code validations on Github actions
Automate code validation on hook
Implement a bot to pre-review the PRs

Resources

Golang hooks and Github actions

Contribute to terraform-provider-libvirt by pinvernizzi

Description

The SUSE Manager (SUMA) teams' main tool for infrastructure automation, Sumaform, largely relies on terraform-provider-libvirt. That provider is also widely used by other teams, both inside and outside SUSE.

It would be good to help the maintainers of this project and give back to the community around it, after all the amazing work that has been already done.

If you're interested in any of infrastructure automation, Terraform, virtualization, tooling development, Go (...) it is also a good chance to learn a bit about them all by putting your hands on an interesting, real-use-case and complex project.

Goals

Get more familiar with Terraform provider development and libvirt bindings in Go
Solve some issues and/or implement some features
Get in touch with the community around the project

Resources

toptop - a top clone written in Go by dshah

Description

toptop is a clone of Linux's top CLI tool, but written in Go.

Goals

Learn more about Go (mainly bubbletea) and Linux

Resources

GitHub

Cluster API Add-on Provider for Kubewarden by csalas

Description

Can we integrate Kubewarden with Cluster API provisioning?

Cluster API is a Kubernetes project focused on providing declarative APIs and tooling to simplify provisioning, upgrading, and operating multiple Kubernetes clusters. TLDR; CAPI let's you define Kubernetes clusters in plain YAML, and CAPI providers (infrastructure, control plane/bootstrap, etc.) manage provisioning and configuration for you.

What if we could create an add-on provider that automatically installs Kubewarden and deploys Policy Servers to CAPI clusters?

Goals

As a user I'd like to set a cluster (or list of clusters) and have the provider install Kubewarden for me.
As a user I'd like to set what policies must be enforced for a cluster (or list of clusters).

Resources

Cluster API: https://cluster-api.sigs.k8s.io/
Kubewarden: https://docs.kubewarden.io/

Metrics Server viewer for Kubernetes by bkampen

This project is finished please visit the github repo below for the tool.

Description

Build a CLI tools which can visualize Kubernetes metrics from the metrics-server, so you're able to watch these without installing Prometheus and Grafana on a cluster.

Goals

Learn more about metrics-server
Learn more about the inner workings of Kubernetes.
Learn more about Go

Resources

https://github.com/bvankampen/metrics-viewer