Project Description
The SINK project is a set of containers and tools for Samba in Kubernetes. The images and tooling for the project are designed for Fedora and RH systems. The ultimate goal of this project is to tweak the tooling for use with our build service, etc.
Goal for this Hackweek
Get network:samba:CONTAINERS to build the samba images, and submit changes to upstream SINK project. Ideally we can have different base images, but for now our containers are being hosted from the github openSUSE org.
Resources
https://github.com/samba-in-kubernetes https://github.com/openSUSE/samba-container
Looking for hackers with the skills:
This project is part of:
Hack Week 22
Activity
Comments
Be the first to comment!
Similar Projects
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
- https://github.com/ori-edge/k8s_gateway/issues/36
- https://github.com/coredns/coredns/issues/2465#issuecomment-593910983
EDIT: Feature mostly complete. An unfinished PR lies here. Successfully tested working on homelab cluster.
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
ddflare: (Dynamic)DNS management via Cloudflare API in Kubernetes by fgiudici
Description
ddflare is a project started a couple of weeks ago to provide DDNS management using v4 Cloudflare APIs: Cloudflare offers management via APIs and access tokens, so it is possible to register a domain and implement a DynDNS client without any other external service but their API.
Since ddflare allows to set any IP to any domain name, one could manage multiple A and ALIAS domain records. Wouldn't be cool to allow full DNS control from the project and integrate it with your Kubernetes cluster?
Goals
Main goals are:
- add containerized image for ddflare
- extend ddflare to be able to add and remove DNS records (and not just update existing ones)
- add documentation, covering also a sample pod deployment for Kubernetes
- write a ddflare Kubernetes operator to enable domain management via Kubernetes resources (using kubebuilder)
Available tasks and improvements tracked on ddflare github.
Resources
- https://github.com/fgiudici/ddflare
- https://developers.cloudflare.com/api/
- https://book.kubebuilder.io
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.
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
SMB3 Server written entirely in Rust by dmulder
Description
Given the number of bugs frequently discovered in the Samba code caused by memory issues, it makes sense to re-write the smbd service purely in Rust code. Meanwhile, it would be wise to abandon backwards compatibility here with insecure protocol versions, and simply implement the SMB3 spec.
Goals
Get a simple server up and running and get it merged into upstream Samba (which now has Rust build support).
Resources
Modularization and Modernization of cifs.ko for Enhanced SMB Protocol Support by hcarvalho
Creator:
Enzo Matsumiya ematsumiya@suse.de @ SUSE Samba team
Members:
Henrique Carvalho henrique.carvalho@suse.com @ SUSE Samba team
Description
Split cifs.ko in 2 separate modules; one for SMB 1.0 and 2.0.x, and another for SMB 2.1, 3.0, and 3.1.1.
Goals
Primary
Start phasing out/deprecation of older SMB versions
Secondary
- Clean up of the code (with focus on the newer versions)
- Update cifs-utils
- Update documentation
- Improve backport workflow (see below)
Technical details
Ideas for the implementation.
- fs/smb/client/{old,new}.c to generate the respective modules
- Maybe don't create separate folders? (re-evaluate as things progresses!)
- Remove server->{ops,vals} if possible
- Clean up fs_context.* -- merge duplicate options into one, handle them in userspace utils
- Reduce code in smb2pdu.c -- tons of functions with very similar init/setup -> send/recv -> handle/free flow
- Restructure multichannel
- Treat initial connection as "channel 0" regardless of multichannel enabled/negotiated status, proceed with extra channels accordingly
- Extra channel just point to "channel 0" as the primary server, no need to allocate an extra TCPServerInfo for each one
- Authentication mechanisms
- Modernize algorithms (references: himmelblau, IAKERB/Local KDC, SCRAM, oauth2 (Azure), etc.
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
ADS-B receiver with MicroOS by epaolantonio
I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time?
There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090
) and web frontend (tar1090
).
Goals
- Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
- Make it easy to install
- Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)
Resources
- 1x Small Board Computer capable of running MicroOS
- 1x RTL2832U DVB-T dongle
- 1x MicroSD card
- https://github.com/antirez/dump1090
- https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
- https://github.com/wiedehopf/tar1090
Project status (2024-11-22)
So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb
(as a replacement for dump1090
), tar1090
, tar1090-db
and mlat-client
(not used yet).
Current status:
- Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
- Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
- Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
- Local web interface (tar1090) to easily visualize the results
- Cockpit pre-configured to ease maintenance
What's missing:
- MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
- FlightAware support
Give it a go at https://g7.github.io/adsbreceiver/ !
Project links
- https://g7.github.io/adsbreceiver/
- https://github.com/g7/adsbreceiver
- https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver
Improve Development Environment on Uyuni by mbussolotto
Description
Currently create a dev environment on Uyuni might be complicated. The steps are:
- add the correct repo
- download packages
- configure your IDE (checkstyle, format rules, sonarlint....)
- setup debug environment
- ...
The current doc can be improved: some information are hard to be find out, some others are completely missing.
Dev Container might solve this situation.
Goals
Uyuni development in no time:
- using VSCode:
- setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
- dev container should contains all dependencies
- setup debug environment
- implement a GitHub Workspace solution
- re-write documentation
Lots of pieces are already implemented: we need to connect them in a consistent solution.
Resources
- https://github.com/uyuni-project/uyuni/wiki
Port the classic browser game HackTheNet to PHP 8 by dgedon
Description
The classic browser game HackTheNet from 2004 still runs on PHP 4/5 and MySQL 5 and needs a port to PHP 8 and e.g. MariaDB.
Goals
- Port the game to PHP 8 and MariaDB 11
- Create a container where the game server can simply be started/stopped
Resources
- https://github.com/nodeg/hackthenet
ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini
Description
ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration
and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal
or local installations. However, the goal is to expand its use to encompass all installations of
Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based
configuration config.yml
.
Overview
- Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
- Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
- Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
- Extensibility: Easily extend functionality with custom plugins and configurations.
- Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
- Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.
Features
- distribution and engine independence. Install your favorite kubernetes engine with your package
manager, execute one script and you'll have a complete working environment at your disposal.
- Basic config approach. One single
config.yml
file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...). - Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
- Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
- Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
- One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
- Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.
Planned features (Wishlist / TODOs)
- Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for