operator for s3gw
an invention by gbaccini
Updated almost 3 years ago. No love. 2 followers.

Project Description

Create a K8s CRD for s3gw.
The operator will be written in Go.
The CRD should in the beginning allow an user to create a bucket.

Goal for this Hackweek

The CRD should in the beginning allow an user to create a bucket.

Project

https://github.com/giubacc/s3gw-operator

Looking for hackers with the skills:

golang kubernetes operator s3gw

This project is part of:

Hack Week 22

Activity

  • almost 3 years ago: gbaccini joined this project.
  • almost 3 years ago: gbaccini added keyword "golang" to this project.
  • almost 3 years ago: gbaccini added keyword "kubernetes" to this project.
  • almost 3 years ago: gbaccini added keyword "operator" to this project.
  • almost 3 years ago: gbaccini added keyword "s3gw" to this project.
  • almost 3 years ago: tdehler started this project.
  • almost 3 years ago: gbaccini originated this project.


    • Comments

    Be the first to comment!

    Similar Projects

    Updatecli Autodiscovery supporting WASM plugins by olblak

    Description

    Updatecli is a Golang Update policy engine that allow to write Update policies in YAML manifest. Updatecli already has a plugin ecosystem for common update strategies such as automating Dockerfile or Kubernetes manifest from Git repositories.

    This is what we call autodiscovery where Updatecli generate manifest and apply them dynamically based on some context.

    Obviously, the Updatecli project doesn't accept plugins specific to an organization.

    I saw project using different languages such as python, C#, or JS to generate those manifest.

    It would be great to be able to share and reuse those specific plugins

    During the HackWeek, I'll hang on the Updatecli matrix channel

    https://matrix.to/#/#Updatecli_community:gitter.im

    Goals

    Implement autodiscovery plugins using WASM. I am planning to experiment with https://github.com/extism/extism

    To build a simple WASM autodiscovery plugin and run it from Updatecli

    Resources

    • https://github.com/extism/extism
    • https://github.com/updatecli/updatecli
    • https://www.updatecli.io/docs/core/autodiscovery/
    • https://matrix.to/#/#Updatecli_community:gitter.im

    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

    go-git: unlocking SHA256-based repository cloning ahead of git v3 by pgomes

    Description

    The go-git library implements the git internals in pure Go, so that any Go application can handle not only Git repositories, but also lower-level primitives (e.g. packfiles, idxfiles, etc) without needing to shell out to the git binary.

    The focus for this Hackweek is to fast track key improvements for the project ahead of the upstream release of Git V3, which may take place at some point next year.

    Goals

    Stretch goals

    Resources

    • https://github.com/go-git/go-git/
    • https://go-git.github.io/docs/

    SUSE Health Check Tools by roseswe

    SUSE HC Tools Overview

    A collection of tools written in Bash or Go 1.24++ to make life easier with handling of a bunch of tar.xz balls created by supportconfig.

    Background: For SUSE HC we receive a bunch of supportconfig tar balls to check them for misconfiguration, areas for improvement or future changes.

    Main focus on these HC are High Availability (pacemaker), SLES itself and SAP workloads, esp. around the SUSE best practices.

    Goals

    • Overall improvement of the tools
    • Adding new collectors
    • Add support for SLES16

    Resources

    csv2xls* example.sh go.mod listprodids.txt sumtext* trails.go README.md csv2xls.go exceltest.go go.sum m.sh* sumtext.go vercheck.py* config.ini csvfiles/ getrpm* listprodids* rpmdate.sh* sumxls* verdriver* credtest.go example.py getrpm.go listprodids.go sccfixer.sh* sumxls.go verdriver.go

    docollall.sh* extracthtml.go gethostnamectl* go.sum numastat.go cpuvul* extractcluster.go firmwarebug* gethostnamectl.go m.sh* numastattest.go cpuvul.go extracthtml* firmwarebug.go go.mod numastat* xtr_cib.sh*

    $ getrpm -r pacemaker >> Product ID: 2795 (SUSE Linux Enterprise Server for SAP Applications 15 SP7 x86_64), RPM Name: +--------------+----------------------------+--------+--------------+--------------------+ | Package Name | Version | Arch | Release | Repository | +--------------+----------------------------+--------+--------------+--------------------+ | pacemaker | 2.1.10+20250718.fdf796ebc8 | x86_64 | 150700.3.3.1 | sle-ha/15.7/x86_64 | | pacemaker | 2.1.9+20250410.471584e6a2 | x86_64 | 150700.1.9 | sle-ha/15.7/x86_64 | +--------------+----------------------------+--------+--------------+--------------------+ Total packages found: 2

    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

    Outcome

    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

    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

    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

    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)

    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/