Building a container bootloader
Building a UEFI application that can boot a EFI stubbed linux kernel+initrd from a container store stored in a fat filesystem.
Goal for this Hackweek
- Build a OCI image containing a kernel+initrd
- Build an EFI application that can boot the above kernel
- ...
- Profit!?
- Try it out with UKI!
Resources
Looking for hackers with the skills:
This project is part of:
Hack Week 23
Activity
Comments
Be the first to comment!
Similar Projects
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.
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
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
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
Enable the containerized Uyuni server to run on different host OS by j_renner
Description
The Uyuni server is provided as a container, but we still require it to run on Leap Micro? This is not how people expect to use containerized applications, so it would be great if we tested other host OSs and enabled them by providing builds of necessary tools for (e.g. mgradm). Interesting candidates should be:
- openSUSE Leap
- 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).
Save pytorch models in OCI registries by jguilhermevanz
Description
A prerequisite for running applications in a cloud environment is the presence of a container registry. Another common scenario is users performing machine learning workloads in such environments. However, these types of workloads require dedicated infrastructure to run properly. We can leverage these two facts to help users save resources by storing their machine learning models in OCI registries, similar to how we handle some WebAssembly modules. This approach will save users the resources typically required for a machine learning model repository for the applications they need to run.
Goals
Allow PyTorch users to save and load machine learning models in OCI registries.
Resources