Extending KubeVirtBMC's capability by adding Redfish support by zchang

Description

In Hack Week 23, we delivered a project called KubeBMC (renamed to KubeVirtBMC now), which brings the good old-fashioned IPMI ways to manage virtual machines running on KubeVirt-powered clusters. This opens the possibility of integrating existing bare-metal provisioning solutions like Tinkerbell with virtualized environments. We even received an inquiry about transferring the project to the KubeVirt organization. So, a proposal was filed, which was accepted by the KubeVirt community, and the project was renamed after that. We have many tasks on our to-do list. Some of them are administrative tasks; some are feature-related. One of the most requested features is Redfish support.

Goals

Extend the capability of KubeVirtBMC by adding Redfish support. Currently, the virtbmc component only exposes IPMI endpoints. We need to implement another simulator to expose Redfish endpoints, as we did with the IPMI module. We aim at a basic set of functionalities:

• Power management
• Boot device selection
• Virtual media mount (this one is not so basic )

Resources

• DMTF/Redfish-Interface-Emulator: The Redfish Interface Emulator can emulate a Redfish-based interface statically (GET) or dynamically (POST, PATCH, DELETE)

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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

• https://kanidm.github.io/kanidm/stable/

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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!

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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):
  • https://github.com/moio/TabletopGames/tree/hackweek24
  • https://github.com/moio/PyTAG/tree/hackweek24

./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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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

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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

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Selenium with Python by xguo

Description

Try to create test case about Selenium base on Python

Goals

• Knowledge about Selenium with Python
• Create new test case about Selenium

Resources

https://selenium-python.readthedocs.io/ https://www.selenium.dev/

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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"

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Enhance UV openQA helper script by mdonis

Description

A couple months ago an UV openQA helper script was created to help/automate the searching phase inside openQA for a given MU to test. The script searches inside all our openQA job groups (qam-sle) related with a given MU and generates an output suitable to add (copy & paste) inside the update log.

This is still a WIP and could use some enhancements.

Goals

• Move script from bash to python: this would be useful in case we want to include this into MTUI in the future. The script will be separate from MTUI for now. The idea is to have this as a CLI tool using the click library or something similar.
• Add option to look for jobs in other sections inside aggregated updates: right now, when looking for regression tests under aggregated updates for a given MU, the script only looks inside the Core MU job group. This is where most of the regression tests we need are located, but some MUs have their regression tests under the YaST/Containers/Security MU job groups. We should keep the Core MU group as a default, but add an option to be able to look into other job groups under aggregated updates.
• Remove the -a option: this option is used to indicate the update ID and is mandatory right now. This is a bit weird and goes against posix stardards. It was developed this way in order to avoid using positional parameters. This problem should be fixed if we move the script to python.

Some other ideas to consider:

• Look into the QAM dashboard API. This has more info on each MU, could use this to link general openQA build results, whether the related RR is approved or not, etc
• Make it easier to see if there's regression tests for a package in an openQA test build. Check if there's a possibility to search for tests that have the package name in them inside each testsuite.
• Unit testing?

More ideas TBD

Resources

https://github.com/os-autoinst/scripts/blob/master/openqa-search-maintenance-core-jobs

https://confluence.suse.com/display/maintenanceqa/Guide+on+how+to+test+Updates

Post-Hackweek update

All major features were implemented. Unit tests are still in progress, and project will be moved to the SUSE github org once everything's done. https://github.com/mjdonis/oqa-search

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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.

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CVE portal for SUSE Rancher products by gmacedo

Description

Currently it's a bit difficult for users to quickly see the list of CVEs affecting images in Rancher, RKE2, Harvester and Longhorn releases. Users need to individually look for each CVE in the SUSE CVE database page - https://www.suse.com/security/cve/ . This is not optimal, because those CVE pages are a bit hard to read and contain data for all SLE and BCI products too, making it difficult to easily see only the CVEs affecting the latest release of Rancher, for example. We understand that certain costumers are only looking for CVE data for Rancher and not SLE or BCI.

Goals

The objective is to create a simple to read and navigate page that contains only CVE data related to Rancher, RKE2, Harvester and Longhorn, where it's easy to search by a CVE ID, an image name or a release version. The page should also provide the raw data as an exportable CSV file.

It must be an MVP with the minimal amount of effort/time invested, but still providing great value to our users and saving the wasted time that the Rancher Security team needs to spend by manually sharing such data. It might not be long lived, as it can be replaced in 2-3 years with a better SUSE wide solution.

Resources

• The page must be simple and easy to read.
• The UI/UX must be as straightforward as possible with minimal visual noise.
• The content must be created automatically from the raw data that we already have internally.
• It must be updated automatically on a daily basis and on ad-hoc runs (when needed).
• The CVE status must be aligned with VEX.
• The raw data must be exportable as CSV file.
• Ideally it will be written in Go or pure Shell script with basic HTML and no external dependencies in CSS or JS.

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Cluster API Provider for Harvester by rcase

Project Description

The Cluster API "infrastructure provider" for Harvester, also named CAPHV, makes it possible to use Harvester with Cluster API. This enables people and organisations to create Kubernetes clusters running on VMs created by Harvester using a declarative spec.

The project has been bootstrapped in HackWeek 23, and its code is available here.

Work done in HackWeek 2023

• Have a early working version of the provider available on Rancher Sandbox : *DONE *
• Demonstrated the created cluster can be imported using Rancher Turtles: DONE
• Stretch goal - demonstrate using the new provider with CAPRKE2: DONE and the templates are available on the repo

Goals for HackWeek 2024

• Add support for ClusterClass
• Add e2e testing
• Add more Unit Tests
• Improve Status Conditions to reflect current state of Infrastructure
• Improve CI (some bugs for release creation)
• Testing with newer Harvester version (v1.3.X and v1.4.X)
• Due to the length and complexity of the templates, maybe package some of them as Helm Charts.
• Other improvement suggestions are welcome!

DONE in HackWeek 24:

• Add more Unit Tests
• Improve Status Conditions for some phases
• Add cloud provider config generation
• Testing with Harvester v1.3.2
• Template improvements
• Issues creation

Thanks to @isim and Dominic Giebert for their contributions!

Resources

Looking for help from anyone interested in Cluster API (CAPI) or who wants to learn more about Harvester.

This will be an infrastructure provider for Cluster API. Some background reading for the CAPI aspect:

• Cluster infrastructure provider contract
• Machine infrastructure provider contract
• Provider implementers guide

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Rancher/k8s Trouble-Maker by tonyhansen

Project Description

When studying for my RHCSA, I found trouble-maker, which is a program that breaks a Linux OS and requires you to fix it. I want to create something similar for Rancher/k8s that can allow for troubleshooting an unknown environment.

Goal for this Hackweek

Create a basic framework for creating Rancher/k8s cluster lab environments as needed for the Break/Fix Create at least 5 modules that can be applied to the cluster and require troubleshooting

Resources

https://github.com/rancher/terraform-provider-rancher2 https://github.com/rancher/tf-rancher-up

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Rancher microfrontend extensions by ftorchia

Description

Rancher UI Extensions allow users, developers, partners, and customers to extend and enhance the Rancher UI. Extensions are Helm charts that can only be installed once into a cluster. The charts contain a UI built package that is downloaded and linked to the Host UI at runtime; this means that the extension pkg needs to be implemented using the same technology and have the same APIs as Rancher UI.

Goals

We want to create a new type of Rancher extension, based on microfrontend pattern. The extension is served in a docker container in the k8s clusters and embedded in the host UI; this would guarantee us to be able to create extensions unrelated to the rancher UI architecture, in any technology.

Non Goals

We want to apply the microfrontend pattern to the product-level extensions; we don't want to apply it to cluster-level extensions.

Resources

rancher-extension-microfrontend, Rancher extensions

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Build Edge Image Builder ISO with SUSE Manager by mweiss2

Description

With SUSE Manager, we can build OS Images using KIWI and container images. As we have Edge Image Builder, we want to see if it is possible to use SUSE Manager to build/customize OS Images by integrating Edge Image Builder as well.

Goals

To make the process easier for customers, a single-build pipeline that automatically adds the combustion and artifact files from the EIB process is desirable.

• Kiwi and EIB need to come from a Git Repository.
• Kiwi and EIB need to be running as containers.
• Configuration options for the images used for Kiwi and EIB build.
• X86 and ARM64 Support.
• SUSE Manager 4.3 and 5.X Support.
• SLES 15 SP6 / SL Micro 6.0 and SL Micro 6.1 Support.

Outcome

• Change the Kiwi build process to use Podman with the Kiwi image registry.suse.com/bci/kiwi:10.1.10
• Change the Edge Image Builder to produce a combustion-only ISO
• Extract the contents and write them to a dedicated /OEM partition integrated via Kiwi into the ISO Kiwi creates.

Sources and PRs

• https://github.com/Martin-Weiss/kiwi-image-micro-gpu-60
• https://github.com/suse-edge/edge-image-builder/pull/618
• https://github.com/uyuni-project/uyuni/pull/9507

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Edge Image Builder and mkosi for Uyuni by oholecek

Description

One part of Uyuni system management tool is ability to build custom images. Currently Uyuni supports only Kiwi image builder.

Kiwi however is not the only image building system out there and with the goal to also become familiar with other systems, this projects aim to add support for Edge Image builder and systemd's mkosi systems.

Goals

Uyuni is able to

• provision EIB and mkosi build hosts
• build EIB and mkosi images and store them

Resources

• Uyuni - https://github.com/uyuni-project/uyuni
• Edge Image builder - https://github.com/suse-edge/edge-image-builder
• mkosi - https://github.com/systemd/mkosi

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