Description

Backstage (backstage.io) is an open-source, CNCF project that allows you to create your own developer portal. There are many plugins for Backstage.

This could be a great compliment to Rancher Manager.

Goals

Learn and experiment with Backstage and look at how this could be integrated with Rancher Manager. Goal is to have some kind of integration completed in this Hack week.

Progress

Screen shot of home page at the end of Hackweek:

Home

Day One

Got Backstage running locally, understanding configuration with HTTPs.
Got Backstage embedded in an IFRAME inside of Rancher
Added content into the software catalog (see: https://backstage.io/docs/features/techdocs/getting-started/)
Understood more about the entity model

Day Two

Connected Backstage to the Rancher local cluster and configured the Kubernetes plugin.
Created Rancher theme to make the light theme more consistent with Rancher

Home

Days Three and Day Four

Created two backend plugins for Backstage:
1. Catalog Entity Provider - this imports users from Rancher into Backstage
2. Auth Provider - uses the proxied sign-in pattern to check the Rancher session cookie, to user that to authenticate the user with Rancher and then log them into Backstage by connecting this to the imported User entity from the catalog entity provider plugin.
With this in place, you can single-sign-on between Rancher and Backstage when it is deployed within Rancher. Note this is only when running locally for development at present

Home

Day Five

Start to build out a production deployment for all of the above
Made some progress, but hit issues with the authentication and proxying when running proxied within Rancher, which needs further investigation

Looking for hackers with the skills:

rancher kubernetes extensions

This project is part of:

Hack Week 24

Activity

about 1 year ago: nwmacd liked this project.

about 1 year ago: nwmacd added keyword "extensions" to this project.

about 1 year ago: nwmacd added keyword "rancher" to this project.

about 1 year ago: nwmacd added keyword "kubernetes" to this project.

about 1 year ago: jadamek liked this project.

about 1 year ago: wombelix liked this project.

about 1 year ago: nwmacd started this project.

about 1 year ago: nwmacd originated this project.

Comments

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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)

Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

Self-Scaling LLM Infrastructure Powered by Rancher

Description

The Problem

Running LLMs can get expensive and complex pretty quickly.

Today there are typically two choices:

Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
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

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

Rancher Cluster Lifecycle Visualizer by jferraz

Description

Rancher’s v2 provisioning system represents each downstream cluster with several Kubernetes custom resources across multiple API groups, such as clusters.provisioning.cattle.io and clusters.management.cattle.io. Understanding why a cluster is stuck in states like "Provisioning", "Updating", or "Unavailable" often requires jumping between these resources, reading conditions, and correlating them with agent connectivity and known failure modes. This project will build a Cluster Lifecycle Visualizer: a small, read-only controller that runs in the Rancher management cluster and generates a single, human-friendly view per cluster. It will watch Rancher cluster CRDs, derive a simplified lifecycle phase, keep a history of phase transitions from installation time onward, and attach a short, actionable recommendation string that hints at what the operator should check or do next.

Goals

Provide a compact lifecycle summary for each Rancher-managed cluster (e.g. Provisioning, WaitingForClusterAgent, Active, Updating, Error) derived from provisioning.cattle.io/v1 Cluster and management.cattle.io/v3 Cluster status and conditions.
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Attach a recommended action to the current phase using a small ruleset based on common Rancher failure modes (for example, cluster agent not connected, cluster still stabilizing after an upgrade, or generic error states), to improve the day-to-day debugging experience.
Deliver an easy-to-install, read-only component (single YAML or small Helm chart) that Rancher users can deploy to their management cluster and inspect via kubectl get/describe, without UI changes or direct access to downstream clusters.
Use idiomatic Go, wrangler, and Rancher APIs.

Resources

Rancher Manager documentation on RKE2 and K3s cluster configuration and provisioning flows.
Rancher API Go types for provisioning.cattle.io/v1 and management.cattle.io/v3 (from the rancher/rancher repository or published Go packages).
Existing Rancher architecture docs and internal notes about cluster provisioning, cluster agents, and node agents.
A local Rancher management cluster (k3s or RKE2) with a few test downstream clusters to validate phase detection, history tracking, and recommendations.

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.

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

kubernetes

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.

The Plan

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?

The CONCLUSION!!!

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

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
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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/

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

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Goals

By the end of Hack Week, the project should:

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
Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.
Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).
Prepare a short demo explaining the end-to-end process and how new models flow through the system.

Resources

Project Repository

Updates

Training pipeline and datasets
Inference Service py

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.

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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

Description

The Problem

Running LLMs can get expensive and complex pretty quickly.

Today there are typically two choices:

Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
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 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