Set Up an Ephemeral Uyuni Instance by mbussolotto

Description

To test, check, and verify the latest changes in the master branch, we want to easily set up an ephemeral environment.

Goals

• Create an ephemeral environment manually

• Create an ephemeral environment automatically

  Resources

• https://github.com/uyuni-project/uyuni

• https://www.uyuni-project.org/uyuni-docs/en/uyuni/index.html

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Ansible to Salt integration by vizhestkov

Description

We already have initial integration of Ansible in Salt with the possibility to run playbooks from the salt-master on the salt-minion used as an Ansible Control node.

In this project I want to check if it possible to make Ansible working on the transport of Salt. Basically run playbooks with Ansible through existing established Salt (ZeroMQ) transport and not using ssh at all.

It could be a good solution for the end users to reuse Ansible playbooks or run Ansible modules they got used to with no effort of complex configuration with existing Salt (or Uyuni/SUSE Multi Linux Manager) infrastructure.

Goals

• [v] Prepare the testing environment with Salt and Ansible installed
• [v] Discover Ansible codebase to figure out possible ways of integration
• [v] Create Salt/Uyuni inventory module
• [v] Make basic modules to work with no using separate ssh connection, but reusing existing Salt connection
• [v] Test some most basic playbooks

Resources

GitHub page

Video of the demo

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Uyuni read-only replica by cbosdonnat

Description

For now, there is no possible HA setup for Uyuni. The idea is to explore setting up a read-only shadow instance of an Uyuni and make it as useful as possible.

Possible things to look at:

• live sync of the database, probably using the WAL. Some of the tables may have to be skipped or some features disabled on the RO instance (taskomatic, PXT sessions…)
• Can we use a load balancer that routes read-only queries to either instance and the other to the RW one? For example, packages or PXE data can be served by both, the API GET requests too. The rest would be RW.

Goals

• Prepare a document explaining how to do it.
• PR with the needed code changes to support it

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Uyuni Health-check Grafana AI Troubleshooter by ygutierrez

Description

This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

Goals

• Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
• Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
• Evaluate support for local LLMs and external APIs through the plugin.
• Evaluate if and how the Uyuni MCP server could be integrated as another source of information.

Resources

Grafana LMM plug-in

Uyuni Health-check

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mgr-ansible-ssh - Intelligent, Lightweight CLI for Distributed Remote Execution by deve5h

Description

By the end of Hack Week, the target will be to deliver a minimal functional version 1 (MVP) of a custom command-line tool named mgr-ansible-ssh (a unified wrapper for BOTH ad-hoc shell & playbooks) that allows operators to:

1. Execute arbitrary shell commands on thousand of remote machines simultaneously using Ansible Runner with artifacts saved locally.
2. Pass runtime options such as inventory file, remote command string/ playbook execution, parallel forks, limits, dry-run mode, or no-std-ansible-output.
3. Leverage existing SSH trust relationships without additional setup.
4. Provide a clean, intuitive CLI interface with --help for ease of use. It should provide consistent UX & CI-friendly interface.
5. Establish a foundation that can later be extended with advanced features such as logging, grouping, interactive shell mode, safe-command checks, and parallel execution tuning.

The MVP should enable day-to-day operations to efficiently target thousands of machines with a single, consistent interface.

Goals

Primary Goals (MVP):

Build a functional CLI tool (mgr-ansible-ssh) capable of executing shell commands on multiple remote hosts using Ansible Runner. Test the tool across a large distributed environment (1000+ machines) to validate its performance and reliability.

Looking forward to significantly reducing the zypper deployment time across all 351 RMT VM servers in our MLM cluster by eliminating the dependency on the taskomatic service, bringing execution down to a fraction of the current duration. The tool should also support multiple runtime flags, such as:

mgr-ansible-ssh: Remote command execution wrapper using Ansible Runner

Usage: mgr-ansible-ssh [--help] [--version] [--inventory INVENTORY]
                   [--run RUN] [--playbook PLAYBOOK] [--limit LIMIT]
                   [--forks FORKS] [--dry-run] [--no-ansible-output]

Required Arguments
--inventory, -i      Path to Ansible inventory file to use

Any One of the Arguments Is Required
--run, -r            Execute the specified shell command on target hosts
--playbook, -p       Execute the specified Ansible playbook on target hosts

Optional Arguments
--help, -h           Show the help message and exit
--version, -v        Show the version and exit
--limit, -l          Limit execution to specific hosts or groups
--forks, -f          Number of parallel Ansible forks
--dry-run            Run in Ansible check mode (requires -p or --playbook)
--no-ansible-output  Suppress Ansible stdout output

Secondary/Stretched Goals (if time permits):

1. Add pretty output formatting (success/failure summary per host).
2. Implement basic logging of executed commands and results.
3. Introduce safety checks for risky commands (shutdown, rm -rf, etc.).
4. Package the tool so it can be installed with pip or stored internally.

Resources

Collaboration is welcome from anyone interested in CLI tooling, automation, or distributed systems. Skills that would be particularly valuable include:

1. Python especially around CLI dev (argparse, click, rich)

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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

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

DONE in 2025 (out of Hackweek)

• Support of ClusterClass
• Add to clusterctl community providers, you can add it directly with clusterctl
• Testing on newer versions of Harvester v1.4.X and v1.5.X
• Support for clusterctl generate cluster ...
• Improve Status Conditions to reflect current state of Infrastructure
• Improve CI (some bugs for release creation)

Goals for HackWeek 2025

• FIRST and FOREMOST, any topic is important to you
• Add e2e testing
• Certify the provider for Rancher Turtles
• Add Machine pool labeling
• Add PCI-e passthrough capabilities.
• Other improvement suggestions are welcome!

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

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Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo

Description

Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.

Goals

Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:

• Gain insight into the latest AI trends, tools, and architectural concepts.
• Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).

Resources

• Red Hat AI Topic Articles

  • https://www.redhat.com/en/topics/ai

• Kubeflow Documentation

  • https://www.kubeflow.org/docs/

• Q4 2025 CNCF Technology Landscape Radar report:

  • https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
  • https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf

• Agent-to-Agent (A2A) Protocol

  • https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/

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Kubernetes-Based ML Lifecycle Automation by lmiranda

Description

This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.

The pipeline will automate the lifecycle of a machine learning model, including:

• Data ingestion/collection
• Model training as a Kubernetes Job
• Model artifact storage in an S3-compatible registry (e.g. Minio)
• A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
• A lightweight inference service that loads and serves the latest model
• Monitoring of model performance and service health through Prometheus/Grafana

The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.

Goals

By the end of Hack Week, the project should:

1. Produce a fully functional ML pipeline running on Kubernetes with:

   • Data collection job
   • Training job container
   • Storage and versioning of trained models
   • Automated deployment of new model versions
   • Model inference API service
   • Basic monitoring dashboards

2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

Resources

Project Repository

Updates

1. Training pipeline and datasets
2. Inference Service py

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OpenPlatform Self-Service Portal by tmuntan1

Description

In SUSE IT, we developed an internal developer platform for our engineers using SUSE technologies such as RKE2, SUSE Virtualization, and Rancher. While it works well for our existing users, the onboarding process could be better.

To improve our customer experience, I would like to build a self-service portal to make it easy for people to accomplish common actions. To get started, I would have the portal create Jira SD tickets for our customers to have better information in our tickets, but eventually I want to add automation to reduce our workload.

Goals

• Build a frontend website (Angular) that helps customers create Jira SD tickets.
• Build a backend (Rust with Axum) for the backend, which would do all the hard work for the frontend.

Resources (SUSE VPN only)

• development site: https://ui-dev.openplatform.suse.com/login?returnUrl=%2Fopenplatform%2Fforms
• https://gitlab.suse.de/itpe/core/open-platform/op-portal/backend
• https://gitlab.suse.de/itpe/core/open-platform/op-portal/frontend

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Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

Self-Scaling LLM Infrastructure Powered by Rancher

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Description

The Problem

Running LLMs can get expensive and complex pretty quickly.

Today there are typically two choices:

1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

What if there was a middle ground?

What if infrastructure scaled itself instead of making you scale it?

Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?

Project Repository: github.com/alexander-demicev/llmserverless

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What This Project Does

A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

A complete, self-scaling LLM infrastructure that:

• Scales to zero when idle (no idle costs)
• Scales up automatically when requests come in
• Adds more nodes when needed, removes them when demand drops
• Runs on any infrastructure - laptop, bare metal, or cloud

Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

How It Works

A combination of open source tools working together:

Flow:

• Users interact with OpenWebUI (chat interface)
• Requests go to LiteLLM Gateway
• LiteLLM routes requests to:
  • Ollama (Knative) for local model inference (auto-scales pods)
  • Or cloud APIs for fallback

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A CLI for Harvester by mohamed.belgaied

Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI. Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.

Project Description

Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as: harvester vm create my-vm --count 5 to create 5 VMs named my-vm-01 to my-vm-05.

Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.

Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli

Done in previous Hackweeks

• Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
• Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE

Goal for this Hackweek

The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.

Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it

Issue list is here: https://github.com/belgaied2/harvester-cli/issues

Resources

The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact). Welcome contributions are:

• Testing it and creating issues
• Documentation
• Go code improvement

What you might learn

Harvester CLI might be interesting to you if you want to learn more about:

• GitHub Actions
• Harvester as a SUSE Product
• Go programming language
• Kubernetes API
• Kubevirt API objects (Manipulating VMs and VM Configuration in Kubernetes using Kubevirt)

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terraform-provider-feilong by e_bischoff

Project Description

People need to test operating systems and applications on s390 platform. While this is straightforward with KVM, this is very difficult with z/VM.

IBM Cloud Infrastructure Center (ICIC) harnesses the Feilong API, but you can use Feilong without installing ICIC(see this schema).

What about writing a terraform Feilong provider, just like we have the terraform libvirt provider? That would allow to transparently call Feilong from your main.tf files to deploy and destroy resources on your z/VM system.

Goal for Hackweek 23

I would like to be able to easily deploy and provision VMs automatically on a z/VM system, in a way that people might enjoy even outside of SUSE.

My technical preference is to write a terraform provider plugin, as it is the approach that involves the least software components for our deployments, while remaining clean, and compatible with our existing development infrastructure.

Goals for Hackweek 24

Feilong provider works and is used internally by SUSE Manager team. Let's push it forward!

Let's add support for fiberchannel disks and multipath.

Goals for Hackweek 25

Modernization, maturity, and maintenance: support for SLES 16 and openTofu, new API calls, fixes...

Resources

• Feilong TSC and demo
• Call APIs with custom Framework Providers

Outcome

• provider, for terraform 1.5.5
• provider, for terraform 0.13.4
• go client library, for Feilong API 1.0

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Contribute to terraform-provider-libvirt by pinvernizzi

Description

The SUSE Manager (SUMA) teams' main tool for infrastructure automation, Sumaform, largely relies on terraform-provider-libvirt. That provider is also widely used by other teams, both inside and outside SUSE.

It would be good to help the maintainers of this project and give back to the community around it, after all the amazing work that has been already done.

If you're interested in any of infrastructure automation, Terraform, virtualization, tooling development, Go (...) it is also a good chance to learn a bit about them all by putting your hands on an interesting, real-use-case and complex project.

Goals

• Get more familiar with Terraform provider development and libvirt bindings in Go
• Solve some issues and/or implement some features
• Get in touch with the community around the project

Resources

• CONTRIBUTING readme
• Go libvirt library in use by the project
• Terraform plugin development
• "Good first issue" list

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Q2Boot - A handy QEMU VM launcher by amanzini

Description

Q2Boot (Qemu Quick Boot) is a command-line tool that wraps QEMU to provide a streamlined experience for launching virtual machines. It automatically configures common settings like KVM acceleration, virtio drivers, and networking while allowing customization through both configuration files and command-line options.

The project originally was a personal utility in D, now recently rewritten in idiomatic Go. It lives at repository https://github.com/ilmanzo/q2boot

Goals

Improve the project, testing with different scenarios , address issues and propose new features. It will benefit of some basic integration testing by providing small sample disk images.

Updates

• Dec 1, 2025 : refactor command line options, added structured logging. Released v0.0.2
• Dec 2, 2025 : added external monitor via telnet option
• Dec 4, 2025 : released v0.0.3 with architecture auto-detection
• Dec 5, 2025 : filing new issues and general polishment. Designing E2E testing

Resources

• https://github.com/ilmanzo/q2boot
• https://www.qemu.org/

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Create a go module to wrap happy-compta.fr by cbosdonnat

Description

https://happy-compta.fr is a tool for french work councils simple book keeping. While it does the job, it has no API to work with and it is tedious to enter loads of operations.

Goals

Write a go client module to be used as an API to programmatically manipulate the tool.

Writing an example tool to load data from a CSV file would be good too.

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