Get familiar with k3s
a project by riafarov
Updated almost 5 years ago. 2 hacker ♥️. 1 follower.

Project Description

Kubernetes is widely used nowadays, but for the developers it's hard to test things locally, and many end up running single node setups. k3s is there to address this issue and provides lightweight stack to gain all advantages of the kubernetes with less efforts to run.

Goal

End goal is to get familiar with k3s and consider scenarios where it can be applied in our daily tasks, as well as share received experience with others, either by giving lightning talk or providing write-up.

Results are documented here: https://github.com/rwx788/exercises#k3sk3d I was able to easily launch kubernetes cluster with multiple nodes and run local docker registry to be used in the setup.

Resources

  • https://rancher.com/docs/k3s/latest/en/

Join this project

Looking for hackers with the skills:

kubernetes k3s zeroops

This project is part of:

Hack Week 20

Activity

  • almost 5 years ago: jblainchristen liked this project.
  • almost 5 years ago: mbrugger liked this project.
  • almost 5 years ago: riafarov added keyword "zeroops" to this project.
  • almost 5 years ago: riafarov added keyword "kubernetes" to this project.
  • almost 5 years ago: riafarov added keyword "k3s" to this project.
  • almost 5 years ago: riafarov started this project.
  • almost 5 years ago: riafarov originated this project.


    • Comments

    • jblainchristen
      almost 5 years ago by jblainchristen | Reply

      As one of the engineers working on k3s (and RKE2) I am happy to help if you encounter any roadblocks! If you have access to it you can always find me on the Rancher Labs Slack. I will also be available on Rocket Chat.

      • riafarov
        almost 5 years ago by riafarov | Reply

        Hey! Somehow I've missed your comment. Thanks a lot for your offer. I was able to figure out how to launch lightweight kubernetes cluster, k3s and k3d documentation is quite good and easy to follow. Just some guides in internet have outdated commands listed for the local docker registry. Cheers!

    Similar Projects

    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

    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

    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/

    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