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

Join this project Leave this project

Looking for hackers with the skills:

kubernetes rook ceph python golang

This project is part of:

Hack Week 20

Activity

almost 4 years ago: haass started this project.

almost 4 years ago: haass added keyword "kubernetes" to this project.

almost 4 years ago: haass added keyword "rook" to this project.

almost 4 years ago: haass added keyword "ceph" to this project.

almost 4 years ago: haass added keyword "python" to this project.

almost 4 years ago: haass added keyword "golang" to this project.

almost 4 years ago: haass originated this project.

Comments

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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

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

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

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

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

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

Resources

NFS-Ganesha

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

python

Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez

Description

Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.

Goals

Explore Ollama
Test different models
Fine tuning
Explore possible integration in Uyuni

Resources

https://ollama.com/
https://huggingface.co/
https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/

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
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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
- Writing openQA tests in Python
- Developing in distrobox containers
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.

Team Hedgehogs' Data Observability Dashboard by gsamardzhiev

Description

This project aims to develop a comprehensive Data Observability Dashboard that provides r insights into key aspects of data quality and reliability. The dashboard will track:

Data Freshness: Monitor when data was last updated and flag potential delays.

Data Volume: Track table row counts to detect unexpected surges or drops in data.

Data Distribution: Analyze data for null values, outliers, and anomalies to ensure accuracy.

Data Schema: Track schema changes over time to prevent breaking changes.

The dashboard's aim is to support historical tracking to support proactive data management and enhance data trust across the data function.

Goals

Although the final goal is to create a power bi dashboard that we are able to monitor, our goals is to 1. Create the necessary tables that track the relevant metadata about our current data 2. Automate the process so it runs in a timely manner

Resources

AWS Redshift; AWS Glue, Airflow, Python, SQL

Why Hedgehogs?

Because we like them.

Symbol Relations by hli

Description

There are tools to build function call graphs based on parsing source code, for example, cscope.

This project aims to achieve a similar goal by directly parsing the disasembly (i.e. objdump) of a compiled binary. The assembly code is what the CPU sees, therefore more "direct". This may be useful in certain scenarios, such as gdb/crash debugging.

Detailed description and Demos can be found in the README file:

Supports x86 for now (because my customers only use x86 machines), but support for other architectures can be added easily.

Tested with python3.6

Goals

Any comments are welcome.

Resources

https://github.com/lhb-cafe/SymbolRelations

symrellib.py: mplements the symbol relation graph and the disassembly parser

symrel_tracer*.py: implements tracing (-t option)

symrel.py: "cli parser"

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

golang

A CLI for Harvester by mohamed.belgaied

Project Description

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

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

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.

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

OpenQA Golang api client by hilchev

Description

I would like to make a simple cli tool to communicate with the OpenQA API

Goals

OpenQA has a ton of information that is hard to get via the UI. A tool like this would make my life easier :)
Would potentially make it easier in the future to make UI changes without Perl.
Improve my Golang skills

Resources

https://go.dev/doc/
https://openqa.opensuse.org/api

Mammuthus - The NFS-Ganesha inside Kubernetes controller by vcheng

Description

Goals

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

Resources

NFS-Ganesha

Harvester Packer Plugin by mrohrich

Description

Goals

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