an invention 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 Kubevirt VMs.
- Source2Image utility. Transform your favorite program (python, go, bash, ...) in a container in a matter of minutes, kubectl apply and create it as a Pod or a Deployment quickly.
- Kubevirt VMs startup management. Since your personal cluster might not be up and running all the time this feature will provide basic startup, shutdown, order list commands; it resembles other VM bare metal configuration suites from the past.
- Lightweight k9s console automatically installed as a plugin from the configuration file
- Add other distributions: suse, debian, rocky/rhel, gentoo, MacOS
- Add other kubernetes engines: minicube, KIND, vanilla k8s, CRC
- Monitoring and observation features, alerting with IM notifications (telegram, signal)
- Remote storage, LAN network volumes, S3 buckets, object storage (CEPH, Longhorn)
- Automatic configuration and support for: Nvidia CUDA, Vulkan drivers. Containers downloaded from Nvidia ContainerHub and relative websites should be used directly without additional configuration.
- Cloud Controller Manager (CCM). A Kubernetes control plane component that embeds cloud specific control logic. This component with a specific automation tool easily allows to migrate local working environment to external (private | hybrid | public) clouds.
Project Resources
- github project repository: clusterops
- @andreabenini @SUSE
- complete README.md (document from where this description has been extracted)
- feel free to reach me on slack, email, submit issues, MR, ...
Looking for hackers with the skills:
kubernetes k3s kubevirt kvm operator personal development webui easy containers pods go golang python
This project is part of:
Hack Week 24
Activity
Comments
-
about 1 month ago by andreabenini | Reply
Day one
Project established. github presence in place, hackweek README project created. Basic libraries in place for the installer/removal utility. I'm now considering k3s because it's easy to manage locally, other engines will be added once main results will be achieved.
Adding SUSE OSes will surely be trivial and I can barely add them all in one shot. I'm now focusing on the k8s operator in order to have minimal functionalities available from it: kubevirt, Web UI, network setup, traefik setup (on local lan, not just localhost).
I'm now using kubebuilder for managing kubernetes operator, its first task will be around adding the default kubernetes dashboard to the system -
about 1 month ago by andreabenini | Reply
Day two
Created user's ContainerHub, now you can easily create your images locally and upload them, the hub is also used from kubernetes for fetching images.
First dummy (but working) operator has been created and uploaded to localhost ContainerHub and it can be installed directly in the k3s installation at startup. ContainerHub has been created as a systemd service and automatically configured from the same clusterops installation script.
Forced k3s dependency makes also easy to have them loaded at startup when required.> systemctl enable clusterops # Start clusterops (with ContainerHub) and k3s on startup
> systemctl start clusterops # Start ClusterOps+ContainerHub+k3s manually -
29 days ago by andreabenini | Reply
Day three
Finally Kubevirt has joined the group and now represents one of the important pillars of this software collection, it relies on community made vanilla Operator and it just works as it's supposed to be. System's requirements are basically QEMU+KVM and libvirt on which libvirtd is built. After a simple test withvirt-host-validate
you can easily have it at your disposal. Full integration with basic components and builtin clusterops Operator is not stable yet but results are promising.
YAML example files are ready and they can be customized by users to easily create or import virtual machines on top of kubernetes in literally a matter of minutes. -
29 days ago by andreabenini | Reply
Day Four, integration mashup
Here's an update on the progress:
- I've modified the installer to seamlessly integrate the ContainerHub service, which is now a legitimate systemd service. This service will be automatically created and updated during installation to ensure consistency.
- Dashboard configuration and Kubevirt settings will also be automatically set during installation, streamlining the process and centralizing these components.
- The Kubernetes Operator will utilize the same configuration file and maintain a stable state across changes, even in cases where parts of a working system are intentionally deleted (excluding the operator itself, of course!).
- Final step will be to unify all external yaml files and enable their automatic use based on user requests. -
28 days ago by andreabenini | Reply
Day Five, final thoughts,
All day has been spent refining these addons: ContainerHub, KubeVirt. Removing pending tasks and tidying up the code in the python installer was important too. I finally have a working environment and installation/setup/removal procedures can now be considered stable with K3S.
OS configuration: the installer is now reduced to the minimum and porting between different distributions should be rather easy. I'll start now with all SUSE related linux distro porting: SLES, Tumbleweed, OpenSUSE. It's already working on a low spec laptop (company laptop) but I'm trying to collect more data before declaring it stable.
I'll add all RHEL related distros (Rocky, Alma, Fedora, RHEL) after it and Debian at the end to mark my interest on all these platforms. Minor changes should be applied but from what I've seen there's no real deal on adding platforms. Questions might be tricky with Security Enhanced libraries (selinux and apparmor mostly) but until I keep installation and configurations on user's profiles it won't hurt Security Roles or Domains that much.
I'll surely stick on k3s for a while because I'm mostly interested in refining my builtin operator, it's barely working but I'll now add new features to autorecover intentional (or unintentional) misconfigurations or removing pods, namespaces, features. Final goal is keeping the kubernetes installation healthy from the inside and it should survive to everything but intentionally removing the operator from the inside (but in that case the external setup should recover it too !). -
24 days ago by andreabenini | Reply
Installation process is now stable and it's fully working.
I have added all SUSE related OSes: Tumbleweed, SLES, OpenSUSE and I'm heavily testing them all in order to avoid typos or gross errors; considering where this project came from it's a relevant topic as you might understand. Apparmor might be noisy so I'm also taking some extra care with it.
I'll surely add the platform named 'suse' to the installer in the next few days to ensure everything works as expected, I don't have a real test bed and I'm applying tests on snapshotted images. I'll consider it as Beta RC for a couple of days before release.
Quickly after that I'll surely add a few interesting platforms to me: Rocky/Alma/Fedora based distros and Debian based before adding new engines (minicube will probably be the next one).
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[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.
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SUSE/SAP/KVM Best Practices
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Dev Container might solve this situation.
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Uyuni development in no time:
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Lots of pieces are already implemented: we need to connect them in a consistent solution.
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Description
While we currently have extensive documentation on user-oriented tasks such as adding minions, patching, fine-tuning, etc, there is a notable gap when it comes to centralizing and documenting core functionalities for developers.
The number of functionalities and side tools we have in Uyuni can be overwhelming. It would be nice to have a centralized place with descriptive list of main/core functionalities.
Goals
Create, aggregate and review on the Uyuni wiki a set of resources, focused on developers, that include also some known common problems/troubleshooting.
The documentation will be helpful not only for everyone who is trying to learn the functionalities with all their inner processes like newcomer developers or community enthusiasts, but also for anyone who need a refresh.
Resources
The resources are currently aggregated here: https://github.com/uyuni-project/uyuni/wiki
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- https://github.com/nodeg/hackthenet
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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
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Goals
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- 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
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, tar1090-db
and mlat-client
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Current status:
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- 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:
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- FlightAware support
Give it a go at https://g7.github.io/adsbreceiver/ !
Project links
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- https://github.com/g7/adsbreceiver
- https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver
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
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Description
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- 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).
Technical talks at universities by agamez
Description
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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.
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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
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- Solve some issues and/or implement some features
- Get in touch with the community around the project
Resources
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- Go libvirt library in use by the project
- Terraform plugin development
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Description
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Goals
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- Practice some Go / Rust coding and programming patterns
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- keep a development diary, practice on project documentation
Follow this link for source code repository
- includes development diary
Some ideas for inspiration:
- https://github.com/coding-horror/basic-computer-games
- https://git.imzadi.de/acn/vt100-games
- https://github.com/skx/lighthouse-of-doom
- https://github.com/rothgar/awesome-tuis
- https://www.zq1.de/~bernhard/images/share/geeko/logo.txt
Related projects:
Resources
Python:
Go:
Rust:
Misc:
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
FamilyTrip Planner: A Personalized Travel Planning Platform for Families by pherranz
Description
FamilyTrip Planner is an innovative travel planning application designed to optimize travel experiences for families with children. By integrating APIs for flights, accommodations, and local activities, the app generates complete itineraries tailored to each family’s unique interests and needs. Recommendations are based on customizable parameters such as destination, trip duration, children’s ages, and personal preferences. FamilyTrip Planner not only simplifies the travel planning process but also offers a comprehensive, personalized experience for families.
Goals
This project aims to: - Create a user-friendly platform that assists families in planning complete trips, from flight and accommodation options to recommended family-friendly activities. - Provide intelligent, personalized travel itineraries using artificial intelligence to enhance travel enjoyment and minimize time and cost. - Serve as an educational project for exploring Go programming and artificial intelligence, with the goal of building proficiency in both.
Resources
To develop FamilyTrip Planner, the project will leverage: - APIs such as Skyscanner, Google Places, and TripAdvisor to source real-time information on flights, accommodations, and activities. - Go programming language to manage data integration, API connections, and backend development. - Basic machine learning libraries to implement AI-driven itinerary suggestions tailored to family needs and preferences.
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
Install Uyuni on Kubernetes in cloud-native way by cbosdonnat
Description
For now installing Uyuni on Kubernetes requires running mgradm
on a cluster node... which is not what users would do in the Kubernetes world. The idea is to implement an installation based only on helm charts and probably an operator.
Goals
Install Uyuni from Rancher UI.
Resources
mgradm
code: https://github.com/uyuni-project/uyuni-tools- Uyuni operator: https://github.com/cbosdo/uyuni-operator
Dartboard TUI by IValentin
Description
Our scalability and performance testing swiss-army knife tool Dartboard is a major WIP so why not add more scope creep? Dartboard is a cli tool which enables users to:
- Define a "Dart" config file as YAML which defines the various components to be created/setup when Dartboard runs its commands
- Spin up infrastructure utilizing opentofu/terraform providers
- Setup K3s or RKE2 clusters on the newly created infrastructure
- Deploy Rancher (with or without downstream cluster), rancher-monitoring (Grafana + Prometheus)
- Create resources in-bulk within the newly created Rancher cluster (ConfigMaps, Secrets, Users, Roles, etc.)
- Run various performance and scalability tests via k6
- Export/Import various tracked metrics (WIP)
Given all these features (and the features to come), it can be difficult to onboard and transfer knowledge of the tool. With a TUI, Dartboard's usage complexity can be greatly reduced!
Goals
- Create a TUI for Dartboard's "subcommands"
- Gain more familiarity with Dartboard and create a more user-friendly interface to enable others to use it
- Stretch Create a TUI workflow for generating a Dart file
Resources
https://github.com/charmbracelet/bubbletea
kubectl clone: Seamlessly Clone Kubernetes Resources Across Multiple Rancher Clusters and Projects by dpunia
Description
kubectl clone is a kubectl plugin that empowers users to clone Kubernetes resources across multiple clusters and projects managed by Rancher. It simplifies the process of duplicating resources from one cluster to another or within different namespaces and projects, with optional on-the-fly modifications. This tool enhances multi-cluster resource management, making it invaluable for environments where Rancher orchestrates numerous Kubernetes clusters.
Goals
- Seamless Multi-Cluster Cloning
- Clone Kubernetes resources across clusters/projects with one command.
- Simplifies management, reduces operational effort.
Resources
Rancher & Kubernetes Docs
- Rancher API, Cluster Management, Kubernetes client libraries.
Development Tools
- Kubectl plugin docs, Go programming resources.
Building and Installing the Plugin
- Set Environment Variables: Export the Rancher URL and API token:
export RANCHER_URL="https://rancher.example.com"
export RANCHER_TOKEN="token-xxxxx:xxxxxxxxxxxxxxxxxxxx"
- Build the Plugin: Compile the Go program:
go build -o kubectl-clone ./pkg/
- Install the Plugin:
Move the executable to a directory in your
PATH
:
mv kubectl-clone /usr/local/bin/
Ensure the file is executable:
chmod +x /usr/local/bin/kubectl-clone
- Verify the Plugin Installation: Test the plugin by running:
kubectl clone --help
You should see the usage information for the kubectl-clone
plugin.
Usage Examples
- Clone a Deployment from One Cluster to Another:
kubectl clone --source-cluster c-abc123 --type deployment --name nginx-deployment --target-cluster c-def456 --new-name nginx-deployment-clone
- Clone a Service into Another Namespace and Modify Labels:
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
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 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.
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"
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
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