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Management solution for openSUSE based on Salt-stack
Saltstack is the only configuration management solution that does not look like a ball of hair.
https://github.com/dmacvicar/playground/tree/minimanager-reactjs/python/minimanager is a prototype of a Spacewalk-like console using Spacewalk as the server and client engine.
It uses Python, Flask and React.js.
The goal would be a simple user interface, and not a port of the command line or json files to a web user interface, like most puppet/chef/salt web user interfaces look like.
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Hack Week 11
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Agama installer on-line demo by lslezak
Description
The Agama installer provides a quite complex user interface. We have some screenshots on the web page but as it is basically a web application it would be nice to have some on-line demo where users could click and check it live.
The problem is that the Agama server directly accesses the hardware (storage probing) and loads installation repositories. We cannot easily mock this in the on-line demo so the easiest way is to have just a read-only demo. You could explore the configuration options but you could not change anything, all changes would be ignored.
The read-only demo would be a bit limited but I still think it would be useful for potential users get the feeling of the new Agama installer and get familiar with it before using in a real installation.
As a proof of concept I already created this on-line demo.
The implementation basically builds Agama in two modes - recording mode where it saves all REST API responses and replay mode where it for the REST API requests returns the previously recorded responses. Recording in the browser is inconvenient and error prone, there should be some scripting instead (see below).
Goals
- Create an Agama on-line demo which can be easily tested by users
- The Agama installer is still in alpha phase and in active development, the online demo needs to be easily rebuilt with the latest Agama version
- Ideally there should be some automation so the demo page is rebuilt automatically without any developer interactions (once a day or week?)
TODO
- Use OpenAPI to get all Agama REST API endpoints, write a script which queries all the endpoints automatically and saves the collected data to a file (see this related PR).
- Write a script for starting an Agama VM (use libvirt/qemu?), the script should ensure we always use the same virtual HW so if we need to dump the latest REST API state we get the same (or very similar data). This should ensure the demo page does not change much regarding the storage proposal etc...
- Fix changing the product, currently it gets stuck after clicking the "Select" button.
- Move the mocking data (the recorded REST API responses) outside the Agama sources, it's too big and will be probably often updated. To avoid messing the history keep it in a separate GitHub repository
- Allow changing the UI language
- Display some note (watermark) in the page so it is clear it is a read-only demo (probably with some version or build date to know how old it is)
- Automation for building new demo page from the latest sources. There should be some check which ensures the recorded data still matches the OpenAPI specification.
Changing the UI language
This will be quite tricky because selecting the proper translation file is done on the server side. We would probably need to completely re-implement the logic in the browser side and adapt the server for that.
Also some REST API responses contain translated texts (storage proposal, pattern names in software). We would need to query the respective endpoints in all supported languages and return the correct response in runtime according to the currently selected language.
Resources
- Agama sources
- Experimental proof of concept demo
- The respective source code change
Team Hedgehogs' Data Observability Dashboard by gsamardzhiev
Description
This project aims to develop a comprehensive Data Observability Dashboard that provides r insights into key aspects of data quality and reliability. The dashboard will track:
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.
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
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"
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.
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
New migration tool for Leap by lkocman
Update
I will call a meeting with other interested people at 11:00 CET https://meet.opensuse.org/migrationtool
Description
SLES 16 plans to have no yast tool in it. Leap 16 might keep some bits, however, we need a new tool for Leap to SLES migration, as this was previously handled by a yast2-migration-sle
Goals
A tool able to migrate Leap 16 to SLES 16, I would like to cover also other scenarios within openSUSE, as in many cases users would have to edit repository files manually.
- Leap -> Leap n+1 (minor and major version updates)
- Leap -> SLES docs
- Leap -> Tumbleweed
- Leap -> Slowroll
- Leap Micro -> Leap Micro n+1 (minor and major version updates)
- Leap Micro -> MicroOS
Hackweek 24 update
Marcela and I were working on the project from Brno coworking as well as finalizing pieces after the hackweek. We've tested several migration scenarios and it works. But it needs further polishing and testing.
Projected was renamed to opensuse-migration-tool and was submitted to devel project https://build.opensuse.org/requests/1227281
Repository
https://github.com/openSUSE/opensuse-migration-tool
Out of scope is any migration to an immutable system. I know Richard already has some tool for that.
Resources
Tracker for yast stack reduction code-o-o/leap/features#173 YaST stack reduction
YQPkg - Bringing the Single Package Selection Back to Life by shundhammer
tl;dr
Rip out the high-level YQPackageSelector widget from YaST and make it a standalone Qt program without any YaST dependencies.
See section "Result" at the bottom for the current status after the hack week.
Current Status
See the development status issue at the GitHub repo.
tl;dr: It's usable now with all the key features.
It does real package installation / removal / update with reasonable user feedback.
The Past and the Present
We used to have and still have a powerful software selection with the YaST sw_single module (and the YaST patterns counterpart): You can select software down to the package level, you can easily select one of many available package versions, you can select entire patterns - or just view them and pick individual packages from patterns.
You can search packages based on name, description, "requires" or "provides" level, and many more things.
The Future
YaST is on its way out, to be replaced by the new Agama installer and Cockpit for system administration. Those tools can do many things, but fine-grained package selection is not among them. And there are also no other Open Source tools available for that purpose that even come close to the YaST package selection.
Many aspects of YaST have become obsolete over the years; many subsystems now come with a good default configuration, or they can configure themselves automatically. Just think about sound or X11 configuration; when did you last need to touch them?
For others, the desktops bring their own tools (e.g. printers), or there are FOSS configuration tools (NetworkManager, BlueMan). Most YaST modules are no longer needed, and for many others there is a replacement in tools like Cockpit.
But no longer having a powerful fine-grained package selection like in YaST sw_single will hurt. Big time. At least until there is an adequate replacement, many users will want to keep it.
The Idea
YaST sw_single always revolved around a powerful high-level widget on the abstract UI level. Libyui has low-level widgets like YPushButton, YCheckBox, YInputField, more advanced ones like YTable, YTree; and some few very high-level ones like YPackageSelector and YPatternSelector that do the whole package selection thing alone, working just on the libzypp level and changing the status of packages or patterns there.
For the YaST Qt UI, the YQPackageSelector / YQPatternSelector widgets work purely on the Qt and libzypp level; no other YaST infrastructure involved, in particular no Ruby (or formerly YCP) interpreter, no libyui-level widgets, no bindings between Qt / C++ and Ruby / YaST-core, nothing. So it's not too hard to rip all that part out of YaST and create a standalone program from it.
For the NCurses UI, the NCPackageSelector / NCPatternSelector create a lot of libyui widgets (inheriting YWidget / NCWidget) and use a lot of libyui calls to glue them together; and all that of course still needs a lot of YaST / libyui / libyui-ncurses infrastructure. So NCurses is out of scope here.
Preparatory Work: Initializing the Package Subsystem
To see if this is feasible at all, the existing UI examples needed some fixing to check what is needed on that level. That was the make-or-break decision: Would it be realistically possible to set the needed environment in libzypp up (without being stranded in the middle of that task alone at the end of the hack week)?
Yes, it is: That part is already working:
https://github.com/yast/yast-ycp-ui-bindings/pull/71
Create openSUSE images for Arm/RISC-V boards by avicenzi
Project Description
Create openSUSE images (or test generic EFI images) for Arm and/or RISC-V boards that are not yet supported.
Goal for this Hackweek
Create bootable images of Tumbleweed for SBCs that currently have no images available or are untested.
Consider generic EFI images where possible, as some boards can hold a bootloader.
Document in the openSUSE Wiki how to flash and use the image for a given board.
Boards that I have around and there are no images:
- Rock 3B
- Nano PC T3 Plus
- Lichee RV D1
- StartFive VisionFive (has some image needs testing)
Hack Week 22
Hack Week 21
Resources
Digital art wallpapers for openSUSE Leap and Tumbleweed by lkocman
Description
We've enrolled set of new wallpapers to both Leap 16 and Tumbleweed as part of https://news.opensuse.org/2024/10/26/leap-tw-get-makeovers/
We've previewed digital art wallpapers which were not part of the initial drop. I'd like to spend time on hackweek to finialize my current Taipei (mountains) and Mauritius digital art wallpapers.
Goals
Finalize existing two digital art wallpapers for Leap and Tumbleweed https://github.com/openSUSE/branding/issues/155 Make them available as part of leap16 dir in https://github.com/openSUSE/wallpapers and update (This makes is available to Tumbleweed users as well). Update https://build.opensuse.org/package/show/X11:common:Factory/wallpapers-openSUSE-extra && Leap:16.0 && Factory.
Resources
https://github.com/openSUSE/branding/issues/155 The mauritius draft can be found in https://github.com/lkocman/geo-wallpapers
Update Haskell ecosystem in Tumbleweed to GHC-9.10.x by psimons
Description
We are currently at GHC-9.8.x, which a bit old. So I'd like to take a shot at the latest version of the compiler, GHC-9.10.x. This is gonna be interesting because the new version requires major updates to all kinds of libraries and base packages, which typically means patching lots of packages to make them build again.
Goals
Have working builds of GHC-9.10.x and the required Haskell packages in 'devel:languages:haskell` so that we can compile:
git-annexpandocxmonadcabal-install
Resources
- https://build.opensuse.org/project/show/devel:languages:haskell/
- https://github.com/opensuse-haskell/configuration/
- #discuss-haskell
- https://www.twitch.tv/peti343