k3sup VS kubernetes

# Maintainer: Talha Altinel pkgname=k3sup pkgver=0.13.0 pkgrel=1 pkgdesc='A tool to bootstrap K3s over SSH in < 60s' arch=('x86_64') url='https://github.com/alexellis/k3sup' license=('MIT') depends=('glibc' 'openssh') makedepends=('git' 'go>=1.20') source=("${pkgname}-${pkgver}.tar.gz::https://github.com/alexellis/k3sup/archive/${pkgver}.tar.gz") sha256sums=('24939844ac6de581eb05ef6425c89c32b2d0e22800f1344c19b2164eec846c92') _commit=('1d2e443ea56a355cc6bd0a14a8f8a2661a72f2e8') build() { cd "$pkgname-$pkgver" export CGO_CPPFLAGS="${CPPFLAGS}" export CGO_CFLAGS="${CFLAGS}" export CGO_CXXFLAGS="${CXXFLAGS}" export CGO_LDFLAGS="${LDFLAGS}" export GOFLAGS="-buildmode=pie -trimpath -mod=readonly -modcacherw" go build \ -ldflags "-s -w -X github.com/alexellis/k3sup/cmd.Version=$pkgver -X github.com/alexellis/k3sup/cmd.GitCommit=$_commit" \ -o k3sup \ . for shell in bash fish zsh; do ./k3sup completion "$shell" > "$shell-completion" done } package() { cd "$pkgname-$pkgver" install -Dm755 -t "$pkgdir/usr/bin" k3sup mkdir -p "${pkgdir}/usr/share/bash-completion/completions/" mkdir -p "${pkgdir}/usr/share/zsh/site-functions/" mkdir -p "${pkgdir}/usr/share/fish/vendor_completions.d/" install -Dm644 bash-completion "$pkgdir/usr/share/bash-completion/completions/k3sup" install -Dm644 fish-completion "$pkgdir/usr/share/fish/vendor_completions.d/k3sup.fish" install -Dm644 zsh-completion "$pkgdir/usr/share/zsh/site-functions/_k3sup" install -Dm644 -t "$pkgdir/usr/share/licenses/$pkgname" LICENSE }

I recommend learning docker first, then pick a vps host from vpsbenchmarks, then use k3sup to deploy a kubernetes cluster on that, then follow a getting-started kubernetes tutorial from there. You'll also want to buy a domain name with tld-list and then provision a TLS certificate with cert-manager and letsencrypt (skip steps 1-4 because Google Cloud is overpriced).

I just installed k3s yesterday using k3sup on 6 VMs (3 masters, 3 workers) each with 2GB RAM ( limited by the actual RAM on hardware, for now ) with Ubuntu 22.04 as the base OS.

k3s installed with k3sup, longhorn for storage, kube-vip for API VIP, and MetalLB for service load balancer using local subnet, and of course Rancher.

My applause to Alex Ellis for writing a clear, direct call to arms!

Their work is super useful and interesting. I've added them to my list of sponsorships: https://github.com/sponsors/alexellis

Yeah, this is the answer, but I would use this with K3S: https://github.com/alexellis/k3sup

$ curl -sLS https://get.k3sup.dev | sh x86_64 Downloading package https://github.com/alexellis/k3sup/releases/download/0.12.12/k3sup as /home/ec2-user/k3sup Download complete. ============================================================ The script was run as a user who is unable to write to /usr/local/bin. To complete the installation the following commands may need to be run manually. ============================================================ sudo cp k3sup /usr/local/bin/k3sup ================================================================ alexellis's work on k3sup needs your support https://github.com/sponsors/alexellis ================================================================ No nos devolverá nada, pero podremos correr lo siguiente para saber si k3sup efectivamente se instalo:

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.