Description
Installing an maintaining ceph as storage solution needs a lot of expertise. Rook in combination with Kubernetes tries to make this more convenient. But this is only true if you are familiar with Kubernetes and its peculiarities. This project tries to create a simple tool which creates a K8s cluster providing Ceph-storage.
Goal for this Hackweek
- Create and provide Storage
- Add and remove nodes from/to the cluster
Resources
- Kubernetes
- Rook
- Ceph
Looking for hackers with the skills:
This project is part of:
Hack Week 20
Activity
Comments
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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:
- Catalog Entity Provider - this imports users from Rancher into Backstage
- 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
Harvester Packer Plugin by mrohrich
Description
Hashicorp Packer is an automation tool that allows automatic customized VM image builds - assuming the user has a virtualization tool at their disposal. To make use of Harvester as such a virtualization tool a plugin for Packer needs to be written. With this plugin users could make use of their Harvester cluster to build customized VM images, something they likely want to do if they have a Harvester cluster.
Goals
Write a Packer plugin bridging the gap between Harvester and Packer. Users should be able to create customized VM images using Packer and Harvester with no need to utilize another virtualization platform.
Resources
Hashicorp documentation for building custom plugins for Packer https://developer.hashicorp.com/packer/docs/plugins/creation/custom-builders
Source repository of the Harvester Packer plugin https://github.com/m-ildefons/harvester-packer-plugin
Setup Kanidm as OIDC provider on Kubernetes by jkuzilek
Description
I am planning to upgrade my homelab Kubernetes cluster to the next level and need an OIDC provider for my services, including K8s itself.
Goals
- Successfully configure and deploy Kanidm on homelab cluster
- Integrate with K8s auth
- Integrate with other services (Envoy Gateway, Container Registry, future deployment of Forgejo?)
Resources
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.
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):
./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
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):
./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
Make more sense of openQA test results using AI by livdywan
Description
AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.
User Story
Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?
Goals
- Leverage a chat interface to help Allison
- Create a model from scratch based on data from openQA
- Proof of concept for automated analysis of openQA test results
Bonus
- Use AI to suggest solutions to merge conflicts
- This would need a merge conflict editor that can suggest solving the conflict
- Use image recognition for needles
Resources
Timeline
Day 1
- Conversing with open-webui to teach me how to create a model based on openQA test results
- Asking for example code using TensorFlow in Python
- Discussing log files to explore what to analyze
- Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant
Day 2
- Using NotebookLLM (Gemini) to produce conversational versions of blog posts
- Researching the possibility of creating a project logo with AI
- Asking open-webui, persons with prior experience and conducting a web search for advice
Highlights
- I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
- Convincing the chat interface to produce code specific to my use case required very explicit instructions.
- Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
- Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses
Outcomes
- Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
- Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.
Saline (state deployment control and monitoring tool for SUSE Manager/Uyuni) by vizhestkov
Project Description
Saline is an addition for salt used in SUSE Manager/Uyuni aimed to provide better control and visibility for states deploymend in the large scale environments.
In current state the published version can be used only as a Prometheus exporter and missing some of the key features implemented in PoC (not published). Now it can provide metrics related to salt events and state apply process on the minions. But there is no control on this process implemented yet.
Continue with implementation of the missing features and improve the existing implementation:
authentication (need to decide how it should be/or not related to salt auth)
web service providing the control of states deployment
Goal for this Hackweek
Implement missing key features
Implement the tool for state deployment control with CLI
Resources
https://github.com/openSUSE/saline
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.ymlfile 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
Ansible for add-on management by lmanfredi
Description
Machines can contains various combinations of add-ons and are often modified during the time.
The list of repos can change so I would like to create an automation able to reset the status to a given state, based on metadata available for these machines
Goals
Create an Ansible automation able to take care of add-on (repo list) configuration using metadata as reference
Resources
- Machines
- Repositories
- Developing modules
- Basic VM Guest management
- Module
zypper_repository_list - ansible-collections community.general
Results
Created WIP project Ansible-add-on-openSUSE
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.
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
Jenny Static Site Generator by adam.pickering
Description
For my personal site I have been using hugo. It works, but I am not satisfied: every time I want to make a change (which is infrequently) I have to read through the documentation again to understand how hugo works. I don't find the documentation easy to use, and the structure of the repository that hugo requires is unintuitive/more complex than what I need. So, I have decided to write my own simple static site generator in Go. It is named Jenny, after my wife.
Goals
- Pages can be written in markdown (which is automatically converted to HTML), but other file types are also allowed
- Easy to understand and use
- Intuitive, simple design
- Clear documentation
- Hot reloading
- Binaries provided for download
- Future maintenance is easy
- Automated releases
Resources
https://github.com/adamkpickering/jenny
file-organizer: A CLI Tool for Efficient File Management by okhatavkar
Description
Create a Go-based CLI tool that helps organize files in a specified folder by sorting them into subdirectories based on defined criteria, such as file type or creation date. Users will pass a folder path as an argument, and the tool will process and organize the files within it.
Goals
- Develop Go skills by building a practical command-line application.
- Learn to manage and manipulate files and directories in Go using standard libraries.
- Create a tool that simplifies file management, making it easier to organize and maintain directories.
Resources
- Go Standard Libraries: Utilize os, filepath, and time for file operations.
- CLI Development: Use flag for basic argument parsing or consider cobra for enhanced functionality.
- Go Learning Material: Go by Example and The Go Programming Language Documentation.
Features
- File Type Sorting: Automatically move files into subdirectories based on their extensions (e.g., documents, images, videos).
- Date-Based Organization: Add an option to organize files by creation date into year/month folders.
- User-Friendly CLI: Build intuitive commands and clear outputs for ease of use. This version maintains the core idea of organizing files efficiently while focusing on Go development and practical file management.
suse-rancher-supportconfig by eminguez
Description
Update: Live at https://github.com/e-minguez/suse-rancher-supportconfig
I finally didn't used golang but used gum instead 
SUSE's supportconfig support tool collects data from the SUSE Operating system. Rancher's rancher2_logs_collector.sh support tool does the same for RKE2/K3s.
Wouldn't be nice to have a way to run both and collect all data for SUSE based RKE2/K3s clusters? Wouldn't be even better with a fancy TUI tool like bubbletea?
Ideally the output should be an html page where you can see the logs/data directly from the browser.
Goals
- Familiarize myself with both
supportconfigandrancher2_logs_collector.shtools - Refresh my golang knowledge
- Have something that works at the end of the hackweek ("works" may vary )
- Be better in naming things
Resources
All links provided above as well as huh