A web frontend for the mirrors in the mirrorbrain database to allow the mirror admins to manage their entries themself.
You might know MirrorBrain already: our download redirector and Torrent/Metalink generator used u.a. on download.opensuse.org. It's really a great tool that plays a hidden key role inside the openSUSE infrastructure.
But while the amount of openSUSE mirrors is increasing over the time (currently we have >180 mirrors in our database!), the amount of main administrators for the database itself is not increasing as well.
It happens, that mirrors want to limit the traffic for a specific time (means for us: decreasing the score of this specific mirror) or changing their setup (means for us: adapting the URLs for FTP, HTTP, rsync or the operator Name and Url - or even the Name and Email of the mirror admin). Not thinking about the work for adding new mirrors or removing old ones. Sometimes it might also be enough to disable a mirror for a short time - and re-enable it after the maintenance work is done. All this is currently done manually on request via mail to admin@opensuse.org or mirror@opensuse.org
But as most of the stuff above only affects single mirrors that are already maintained by people who should know what they are doing, why not allowing them to do the requested steps on their own?
Maybe they can even trigger a "rescan" of their mirror once it is added - or something has changed/fixed?
Wouldn't this be cool?
We guess: yes!
Looking for hackers with the skills:
This project is part of:
Hack Week 10 Hack Week 11
Activity
Comments
-
about 11 years ago by lrupp | Reply
Big progress today: Big progress today: * mirrors are listed like on mirrors.opensuse.org but with additional filters (distribution, region and markers), which makes it easier for customers to find "their" mirror * each mirror belongs at least to one admin-group * users in such a group can edit the mirror data * the entered data is validated * the page to register a new mirror is prepared
TO DO: * finish the backend parts to create a new mirror (getting Geo-based UP information, incl. ASN data and prefixes from entered data and more validation) * log all changes * do we need a "go back" button? * add delete button for mirrors * add additional tools like a search engine, "scan now" button, ... * clean-up css and html templates * write a script to create groups and users from current data and assign them to the right servers
So there is still a lot to do, but important basics are there now and we might be able to have something to present real soon!
-
about 11 years ago by lrupp | Reply
Done:
- creating and deleting a mirror works now (thanks to darix!)
- enhanced the web page layout, to have more space for the important data
- merged rails4 branch with master => we will not "ship" a rails3 version any more
- providing a small Google map for the Geo Location of a server
ToDo:
- write a script to create groups and users from current data and assign them to the right servers
- add additional tools like a search engine, "scan now" button, ...
- log all changes * do we need a "go back" button?
- allow users to search for a specific server
- add additional field for "rsync from" addresses, so admins can add the origin IP addresses their servers use to sync from stage.opensuse.org
Similar Projects
Fix RSpec tests in order to replace the ruby-ldap rubygem in OBS by enavarro_suse
Description
"LDAP mode is not official supported by OBS!". See: config/options.yml.example#L100-L102
However, there is an RSpec file which tests LDAP mode in OBS. These tests use the ruby-ldap
rubygem, mocking the results returned by a LDAP server.
The ruby-ldap
rubygem seems no longer maintaned, and also prevents from updating to a more recent Ruby version. A good alternative is to replace it with the net-ldap
rubygem.
Before replacing the ruby-ldap
rubygem, we should modify the tests so the don't mock the responses of a LDAP server. Instead, we should modify the tests and run them against a real LDAP server.
Goals
Goals of this project:
- Modify the RSpec tests and run them against a real LDAP server
- Replace the
net-ldap
rubygem with theruby-ldap
rubygem
Achieving the above mentioned goals will:
- Permit upgrading OBS from Ruby 3.1 to Ruby 3.2
- Make a step towards officially supporting LDAP in OBS.
Resources
Recipes catalog and calculator in Rails 8 by gfilippetti
My wife needs a website to catalog and sell the products of her upcoming bakery, and I need to learn and practice modern Rails. So I'm using this Hack Week to build a modern store using the latest Ruby on Rails best practices, ideally up to the deployment.
TO DO
- Index page
- Product page
- Admin area -- Supplies calculator based on orders -- Orders notification
- Authentication
- Payment
- Deployment
Day 1
As my Rails knowledge was pretty outdated and I had 0 experience with Turbo (wich I want to use in the app), I started following a turbo-rails course. I completed 5 of 11 chapters.
Day 2
Continued the course until chapter 8 and added live updates & an empty state to the app. I should finish the course on day 3 and start my own project with the knowledge from it.
Hackweek 24
For this Hackweek I'll continue this project, focusing on a Catalog/Calculator for my wife's recipes so she can use for her Café.
Day 1
Use local/private LLM for semantic knowledge search by digitaltomm
Description
Use a local LLM, based on SUSE AI (ollama, openwebui) to power geeko search (public instance: https://geeko.port0.org/).
Goals
Build a SUSE internal instance of https://geeko.port0.org/ that can operate on internal resources, crawling confluence.suse.com, gitlab.suse.de, etc.
Resources
Repo: https://github.com/digitaltom/semantic-knowledge-search
Public instance: https://geeko.port0.org/
Results
Internal instance:
I have an internal test instance running which has indexed a couple of internal wiki pages from the SCC team. It's using the ollama (llama3.1:8b
) backend of suse-ai.openplatform.suse.com to create embedding vectors for indexed resources and to create a chat response. The semantic search for documents is done with a vector search inside of sqlite, using sqlite-vec.
Recipes catalog and calculator in Rails 8 by gfilippetti
My wife needs a website to catalog and sell the products of her upcoming bakery, and I need to learn and practice modern Rails. So I'm using this Hack Week to build a modern store using the latest Ruby on Rails best practices, ideally up to the deployment.
TO DO
- Index page
- Product page
- Admin area -- Supplies calculator based on orders -- Orders notification
- Authentication
- Payment
- Deployment
Day 1
As my Rails knowledge was pretty outdated and I had 0 experience with Turbo (wich I want to use in the app), I started following a turbo-rails course. I completed 5 of 11 chapters.
Day 2
Continued the course until chapter 8 and added live updates & an empty state to the app. I should finish the course on day 3 and start my own project with the knowledge from it.
Hackweek 24
For this Hackweek I'll continue this project, focusing on a Catalog/Calculator for my wife's recipes so she can use for her Café.
Day 1
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.
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
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
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
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/
Create object oriented API for perl's YAML::XS module, with YAML 1.2 Support by tinita
Description
YAML::XS is a binding to libyaml and already quite old, but the most popular YAML module for perl. There are two main issues:
- It uses global package variables to influence behaviour.
- It didn't implement the loading of types like numbers and booleans according to the YAML spec (neither 1.1 nor 1.2).
Goals
Create a new interface which works object oriented. Currently YAML::XS exports a list of functions.
- The new API will allow to create a YAML::XS object containing configuration influencing the behaviour of loading and dumping.
- It keeps the libyaml parser and emitter structs in memory, so repeated calls can save the creation of those structs
- It will by default implement the YAML 1.2 Core Schema, so it is compatible to other YAML processors in perl and in other languages
- If I have time, I would like to add the merge
<<
key feature as an option. We could then use it in openQA as a replacement for YAML::PP to be faster.
I already created a proof of concept with a minimal functionality some weeks before this HackWeek.
Resources
- Work is currently happening on the oop branch
- Experimental release waiting for user feedback: https://github.com/perlpunk/yaml-libyaml-pm/releases
- Diff