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 Kubevirt VMs.
  • Source2Image utility. Transform your favorite program (python, go, bash, ...) in a container in a matter of minutes, kubectl apply and create it as a Pod or a Deployment quickly.
  • Kubevirt VMs startup management. Since your personal cluster might not be up and running all the time this feature will provide basic startup, shutdown, order list commands; it resembles other VM bare metal configuration suites from the past.
  • Lightweight k9s console automatically installed as a plugin from the configuration file
  • Add other distributions: suse, debian, rocky/rhel, gentoo, MacOS
  • Add other kubernetes engines: minicube, KIND, vanilla k8s, CRC
  • Monitoring and observation features, alerting with IM notifications (telegram, signal)
  • Remote storage, LAN network volumes, S3 buckets, object storage (CEPH, Longhorn)
  • Automatic configuration and support for: Nvidia CUDA, Vulkan drivers. Containers downloaded from Nvidia ContainerHub and relative websites should be used directly without additional configuration.
  • Cloud Controller Manager (CCM). A Kubernetes control plane component that embeds cloud specific control logic. This component with a specific automation tool easily allows to migrate local working environment to external (private | hybrid | public) clouds.

Project Resources

  • github project repository: clusterops
  • @andreabenini @SUSE
  • complete README.md (document from where this description has been extracted)
  • feel free to reach me on slack, email, submit issues, MR, ...

This project is part of:

Hack Week 24

Activity

  • 22 days ago: andreabenini added keyword "go" to this project.
  • 22 days ago: andreabenini added keyword "golang" to this project.
  • 22 days ago: andreabenini added keyword "python" to this project.
  • 22 days ago: andreabenini liked this project.
  • 22 days ago: andreabenini added keyword "containers" to this project.
  • 22 days ago: andreabenini added keyword "pods" to this project.
  • 22 days ago: andreabenini added keyword "webui" to this project.
  • 22 days ago: andreabenini added keyword "easy" to this project.
  • 22 days ago: andreabenini added keyword "kubernetes" to this project.
  • 22 days ago: andreabenini added keyword "k3s" to this project.
  • 22 days ago: andreabenini added keyword "kubevirt" to this project.
  • 22 days ago: andreabenini added keyword "kvm" to this project.
  • 22 days ago: andreabenini added keyword "operator" to this project.
  • 22 days ago: andreabenini added keyword "personal" to this project.
  • 22 days ago: andreabenini added keyword "development" to this project.
  • 22 days ago: andreabenini started this project.
  • 22 days ago: andreabenini originated this project.

  • Comments

    • andreabenini
      22 days ago by andreabenini | Reply

      Day one
      Project established. github presence in place, hackweek README project created. Basic libraries in place for the installer/removal utility. I'm now considering k3s because it's easy to manage locally, other engines will be added once main results will be achieved.
      Adding SUSE OSes will surely be trivial and I can barely add them all in one shot. I'm now focusing on the k8s operator in order to have minimal functionalities available from it: kubevirt, Web UI, network setup, traefik setup (on local lan, not just localhost).
      I'm now using kubebuilder for managing kubernetes operator, its first task will be around adding the default kubernetes dashboard to the system

    • andreabenini
      21 days ago by andreabenini | Reply

      Day two
      Created user's ContainerHub, now you can easily create your images locally and upload them, the hub is also used from kubernetes for fetching images.
      First dummy (but working) operator has been created and uploaded to localhost ContainerHub and it can be installed directly in the k3s installation at startup. ContainerHub has been created as a systemd service and automatically configured from the same clusterops installation script.
      Forced k3s dependency makes also easy to have them loaded at startup when required.

      > systemctl enable clusterops # Start clusterops (with ContainerHub) and k3s on startup
      > systemctl start clusterops # Start ClusterOps+ContainerHub+k3s manually

    • andreabenini
      20 days ago by andreabenini | Reply

      Day three
      Finally Kubevirt has joined the group and now represents one of the important pillars of this software collection, it relies on community made vanilla Operator and it just works as it's supposed to be. System's requirements are basically QEMU+KVM and libvirt on which libvirtd is built. After a simple test with virt-host-validate you can easily have it at your disposal. Full integration with basic components and builtin clusterops Operator is not stable yet but results are promising.
      YAML example files are ready and they can be customized by users to easily create or import virtual machines on top of kubernetes in literally a matter of minutes.

    • andreabenini
      19 days ago by andreabenini | Reply

      Day Four, integration mashup

      Here's an update on the progress:
      - I've modified the installer to seamlessly integrate the ContainerHub service, which is now a legitimate systemd service. This service will be automatically created and updated during installation to ensure consistency.
      - Dashboard configuration and Kubevirt settings will also be automatically set during installation, streamlining the process and centralizing these components.
      - The Kubernetes Operator will utilize the same configuration file and maintain a stable state across changes, even in cases where parts of a working system are intentionally deleted (excluding the operator itself, of course!).
      - Final step will be to unify all external yaml files and enable their automatic use based on user requests.

    • andreabenini
      18 days ago by andreabenini | Reply

      Day Five, final thoughts,
      All day has been spent refining these addons: ContainerHub, KubeVirt. Removing pending tasks and tidying up the code in the python installer was important too. I finally have a working environment and installation/setup/removal procedures can now be considered stable with K3S.
      OS configuration: the installer is now reduced to the minimum and porting between different distributions should be rather easy. I'll start now with all SUSE related linux distro porting: SLES, Tumbleweed, OpenSUSE. It's already working on a low spec laptop (company laptop) but I'm trying to collect more data before declaring it stable.
      I'll add all RHEL related distros (Rocky, Alma, Fedora, RHEL) after it and Debian at the end to mark my interest on all these platforms. Minor changes should be applied but from what I've seen there's no real deal on adding platforms. Questions might be tricky with Security Enhanced libraries (selinux and apparmor mostly) but until I keep installation and configurations on user's profiles it won't hurt Security Roles or Domains that much.
      I'll surely stick on k3s for a while because I'm mostly interested in refining my builtin operator, it's barely working but I'll now add new features to autorecover intentional (or unintentional) misconfigurations or removing pods, namespaces, features. Final goal is keeping the kubernetes installation healthy from the inside and it should survive to everything but intentionally removing the operator from the inside (but in that case the external setup should recover it too !).

    • andreabenini
      15 days ago by andreabenini | Reply

      Installation process is now stable and it's fully working.
      I have added all SUSE related OSes: Tumbleweed, SLES, OpenSUSE and I'm heavily testing them all in order to avoid typos or gross errors; considering where this project came from it's a relevant topic as you might understand. Apparmor might be noisy so I'm also taking some extra care with it.
      I'll surely add the platform named 'suse' to the installer in the next few days to ensure everything works as expected, I don't have a real test bed and I'm applying tests on snapshotted images. I'll consider it as Beta RC for a couple of days before release.
      Quickly after that I'll surely add a few interesting platforms to me: Rocky/Alma/Fedora based distros and Debian based before adding new engines (minicube will probably be the next one).

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    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


    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.


    Metrics Server viewer for Kubernetes by bkampen

    This project is finished please visit the github repo below for the tool.

    Description

    Build a CLI tools which can visualize Kubernetes metrics from the metrics-server, so you're able to watch these without installing Prometheus and Grafana on a cluster.

    Goals

    • Learn more about metrics-server
    • Learn more about the inner workings of Kubernetes.
    • Learn more about Go

    Resources

    https://github.com/bvankampen/metrics-viewer


    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:

    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:


    ddflare: (Dynamic)DNS management via Cloudflare API in Kubernetes by fgiudici

    Description

    ddflare is a project started a couple of weeks ago to provide DDNS management using v4 Cloudflare APIs: Cloudflare offers management via APIs and access tokens, so it is possible to register a domain and implement a DynDNS client without any other external service but their API.

    Since ddflare allows to set any IP to any domain name, one could manage multiple A and ALIAS domain records. Wouldn't be cool to allow full DNS control from the project and integrate it with your Kubernetes cluster?

    Goals

    Main goals are:

    1. add containerized image for ddflare
    2. extend ddflare to be able to add and remove DNS records (and not just update existing ones)
    3. add documentation, covering also a sample pod deployment for Kubernetes
    4. write a ddflare Kubernetes operator to enable domain management via Kubernetes resources (using kubebuilder)

    Available tasks and improvements tracked on ddflare github.

    Resources

    • https://github.com/fgiudici/ddflare
    • https://developers.cloudflare.com/api/
    • https://book.kubebuilder.io


    Hack on rich terminal user interfaces by amanzini

    Description

    TUIs (Textual User Interface) are a big classic of our daily workflow. Many linux users 'live' in the terminal and modern implementations have a lot to offer : unicode fonts, 24 bit colors etc.

    Goals

    • Explore the current available solution on modern languages and implement a PoC , for example a small maze generator, porting of a classic game or just display the HackWeek cute logo.
    • Practice some Go / Rust coding and programming patterns
    • Fiddle around, hack, learn, have fun
    • keep a development diary, practice on project documentation

    Follow this link for source code repository

    Some ideas for inspiration:

    Related projects:

    Resources


    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


    Dartboard TUI by IValentin

    Description

    Our scalability and performance testing swiss-army knife tool Dartboard is a major WIP so why not add more scope creep? Dartboard is a cli tool which enables users to:

    • Define a "Dart" config file as YAML which defines the various components to be created/setup when Dartboard runs its commands
    • Spin up infrastructure utilizing opentofu/terraform providers
    • Setup K3s or RKE2 clusters on the newly created infrastructure
    • Deploy Rancher (with or without downstream cluster), rancher-monitoring (Grafana + Prometheus)
    • Create resources in-bulk within the newly created Rancher cluster (ConfigMaps, Secrets, Users, Roles, etc.)
    • Run various performance and scalability tests via k6
    • Export/Import various tracked metrics (WIP)

    Given all these features (and the features to come), it can be difficult to onboard and transfer knowledge of the tool. With a TUI, Dartboard's usage complexity can be greatly reduced!

    Goals

    • Create a TUI for Dartboard's "subcommands"
    • Gain more familiarity with Dartboard and create a more user-friendly interface to enable others to use it
    • Stretch Create a TUI workflow for generating a Dart file

    Resources

    https://github.com/charmbracelet/bubbletea


    toptop - a top clone written in Go by dshah

    Description

    toptop is a clone of Linux's top CLI tool, but written in Go.

    Goals

    Learn more about Go (mainly bubbletea) and Linux

    Resources

    GitHub


    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


    A CLI for Harvester by mohamed.belgaied

    [comment]: # Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI [comment]: # Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. [comment]: # 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.

    asciicast

    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


    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


    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

    Day 2

    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.


    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!
    A chameleon playing chess in a train car, as a metaphor of SUSE AI applied to games


    Results: Infrastructure Achievements

    We successfully built and automated a containerized stack to support our AI experiments. This included:

    A screenshot of k9s and nvtop showing PyTAG running in Kubernetes with GPU acceleration

    ./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 .

    Cards from the three games

    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


    Ansible for add-on management by lmanfredi

    Description

    Machines can contains various combinations of add-ons and are often modified during the time.

    The list of repos can change so I would like to create an automation able to reset the status to a given state, based on metadata available for these machines

    Goals

    Create an Ansible automation able to take care of add-on (repo list) configuration using metadata as reference

    Resources

    Results

    Created WIP project Ansible-add-on-openSUSE


    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/