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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Description

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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.
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Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
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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

Harvester Packer Plugin by mrohrich

Description

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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.

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Hashicorp documentation for building custom plugins for Packer https://developer.hashicorp.com/packer/docs/plugins/creation/custom-builders

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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):
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- 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

Multi-pod, autoscalable Elixir application in Kubernetes using K8s resources by socon

Description

Elixir / Erlang use their own solutions to create clusters that work together. Kubernetes provide its own orchestration. Due to the nature of the BEAM, it looks a very promising technology for applications that run in Kubernetes and requite to be always on, specifically if they are created as web pages using Phoenix.

Goals

Investigate and provide solutions that work in Phoenix LiveView using Kubernetes resources, so a multi-pod application can be used
Provide an end to end example that creates and deploy a container from source code.

Resources

https://github.com/dwyl/phoenix-liveview-counter-tutorial https://github.com/propedeutica/elixir-k8s-counter

Technical talks at universities by agamez

Description

This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

Goals

Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

Resources

Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.

python

Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil

Join the Gitter channel! https://gitter.im/uyuni-project/hackweek

Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!

Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.

For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.

No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)

The idea is testing Salt and Salt-ssh clients, but NOT traditional clients, which are deprecated.

To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):

Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
Package management (install, remove, update...)
Patching
Applying any basic salt state (including a formula)
Salt remote commands
Bonus point: Java part for product identification, and monitoring enablement
Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)

If you don't have knowledge about some of the steps: ask the team
If you still don't know what to do: switch to another distribution and keep testing.

This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)

Pending

FUSS

FUSS is a complete GNU/Linux solution (server, client and desktop/standalone) based on Debian for managing an educational network.

https://fuss.bz.it/

Seems to be a Debian 12 derivative, so adding it could be quite easy.

[W] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
[W] Onboarding (salt minion from UI, salt minion from bootstrap script, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator) --> Working for all 3 options (salt minion UI, salt minion bootstrap script and salt-ssh minion from the UI).
[W] Package management (install, remove, update...) --> Installing a new package works, needs to test the rest.
[I] Patching (if patch information is available, could require writing some code to parse it, but IIRC we have support for Ubuntu already). No patches detected. Do we support patches for Debian at all?
[W] Applying any basic salt state (including a formula)
[W] Salt remote commands
[ ] Bonus point: Java part for product identification, and monitoring enablement

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/

Team Hedgehogs' Data Observability Dashboard by gsamardzhiev

Description

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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.

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.
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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.
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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.
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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

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:

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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"

golang

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 supportconfig and rancher2_logs_collector.sh tools
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

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Description

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Goals

Explore the current available solution on modern languages and implement a PoC , for example a small maze generator, porting of a classic game or just display the HackWeek cute logo.
Practice some Go / Rust coding and programming patterns
Fiddle around, hack, learn, have fun
keep a development diary, practice on project documentation

Follow this link for source code repository

includes development diary

Some ideas for inspiration:

Related projects:

https://hackweek.opensuse.org/24/projects/suse-rancher-supportconfig from @eminguez

Resources

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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.

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.
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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.
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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, ...).
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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.
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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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Description

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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

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Date-Based Organization: Add an option to organize files by creation date into year/month folders.
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