externaltools.suse.de kubernetes deployment
a project by digitaltomm
Updated over 6 years ago. 4 hacker ♥️. 1 follower.

Currently externaltools is deployed manually with RPM. This is a manual process and involves packaging gem dependencies.

We do have a caasp cluster running internally which already hosts geekos.scc.suse.de and dash.scc.suse.de.

It would simplify development on externaltools a lot if we could switch it to be automatically deployed in CaaSP.

Links:

https://externaltools.suse.de/

https://gitlab.suse.de/OPS-Service/externaltools/

Example gitlab CI pipeline with caasp deployment (.gitlab-ci.yml, geekos-frontend.yml)

Join this project

Looking for hackers with the skills:

rails caasp kubernetes gitlab

This project is part of:

Hack Week 17

Activity

  • over 7 years ago: farahschueller joined this project.
  • over 7 years ago: skotov liked this project.
  • over 7 years ago: skotov started this project.
  • over 7 years ago: farahschueller liked this project.
  • over 7 years ago: cschum liked this project.
  • over 7 years ago: okurz liked this project.
  • over 7 years ago: digitaltomm added keyword "rails" to this project.
  • over 7 years ago: digitaltomm added keyword "caasp" to this project.
  • over 7 years ago: digitaltomm added keyword "kubernetes" to this project.
  • over 7 years ago: digitaltomm added keyword "gitlab" to this project.
  • over 7 years ago: digitaltomm originated this project.


    • Comments

    • okurz
      over 7 years ago by okurz | Reply

      Hm, sounds interesting. I wonder in general, how does this simplify deployment? Isn't an automatic update of RPM very easy or what is the current approach used?

      • cschum
        over 7 years ago by cschum | Reply

        RPMs are not a natural way to package Rails applications. Containers work better there. And with Kubernetes you also get the necessary configuration of the infrastructure around the application.

        Even simpler would be to use a PaaS system. But as an exercise to learn how to do it with Kubernetes this is an interesting project.

    • kiall
      over 7 years ago by kiall | Reply

      Re the .gitlab-ci.yml you gave - you could also use the new GitLab Kubernetes integration with CaaSP. This can do some cool stuff, like review apps (Deploy a full instance of the stack for each PR, destroying it again when closed or merged)... Check the products "Operations -> Kubernetes" section at the side to add connection details for your cluster.

    Similar Projects

    Recipes catalog and calculator in Rails 8 by gfilippetti

    My wife needs a website to catalog and sell the products of her upcoming bakery, and I need to learn and practice modern Rails. So I'm using this Hack Week to build a modern store using the latest Ruby on Rails best practices, ideally up to the deployment.

    TO DO

    • Index page
    • Product page
    • Admin area -- Supplies calculator based on orders -- Orders notification
    • Authentication
    • Payment
    • Deployment

    Day 1

    As my Rails knowledge was pretty outdated and I had 0 experience with Turbo (wich I want to use in the app), I started following a turbo-rails course. I completed 5 of 11 chapters.

    Day 2

    Continued the course until chapter 8 and added live updates & an empty state to the app. I should finish the course on day 3 and start my own project with the knowledge from it.

    Hackweek 25

    For this Hackweek I'll continue this project, focusing on a Catalog/Calculator for my wife's recipes so she can use for her Café.

    Day 1

    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.

    Goals for Hackweek 25

    • Update to modern Rancher and verify that existing tests still work
    • Change testing logic to populate secrets instead of requiring a secondary script
    • Add new tests

    Goals for Hackweek 24 (Complete)

    • 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/celidon/rancher-troublemaker
    • https://github.com/rancher/terraform-provider-rancher2
    • https://github.com/rancher/tf-rancher-up
    • https://github.com/rancher/quickstart

    The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio

    Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it. A GitHub robot mascot trying to lasso a blue bull with a Kubernetes logo tatooed on it

    The Plan

    Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!

    Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:


    ❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.

    ❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.

    ❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.


    If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.

    Why?

    We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.

    The CONCLUSION!!!

    A add-emoji State of the Union add-emoji document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below! add-emoji

    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo

    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

    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

    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/

    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.

    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
    • Kubevirt API objects (Manipulating VMs and VM Configuration in Kubernetes using Kubevirt)