k0s VS orchest

I am myself studying it and going through the official documentation and toying with k8s flavors like kind, k3s and k0s.

There's a whole community of hobbyists building Raspberry Pi clusters, porting things to work on various Arm processors, exploring and contributing to minimalist distros like k0s and microk8s, etc.

root@localhost:~# curl -sSLf https://get.k0s.sh | sudo sh Downloading k0s from URL: https://github.com/k0sproject/k0s/releases/download/v1.25.4+k0s.0/k0s-v1.25.4+k0s.0-amd64 k0s is now executable in /usr/local/bin root@localhost:~# k0s install controller --single root@localhost:~# k0s start root@localhost:~# k0s status Version: v1.25.4+k0s.0 Process ID: 1064 Role: controller Workloads: true SingleNode: true Kube-api probing successful: true Kube-api probing last error: root@localhost:~# k0s kubectl cluster-info Kubernetes control plane is running at https://localhost:6443 CoreDNS is running at https://localhost:6443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'. 443/TCP 97s root@localhost:~# k0s kubectl get nodes -o wide NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME localhost Ready control-plane 100s v1.25.4+k0s 172.105.131.23 Ubuntu 22.04.1 LTS 5.15.0-47-generic containerd://1.6.9 root@localhost:~# curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.25.4/bin/linux/amd64/kubectl && chmod +x kubectl && mv kubectl /usr/bin/ % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 42.9M 100 42.9M 0 0 75.2M 0 --:--:-- --:--:-- --:--:-- 75.3M root@localhost:~# k0s kubeconfig admin > ~/.kube/config root@localhost:~# type kubectl kubectl is hashed (/usr/bin/kubectl) root@localhost:~# kubectl get po,svc -A NAMESPACE NAME READY STATUS RESTARTS AGE kube-system pod/kube-proxy-clxh7 1/1 Running 0 3m56s kube-system pod/kube-router-88x25 1/1 Running 0 3m56s kube-system pod/coredns-5d5b5b96f9-4xzsl 1/1 Running 0 4m3s kube-system pod/metrics-server-69d9d66ff8-fxrt7 1/1 Running 0 4m2s NAMESPACE NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE default service/kubernetes ClusterIP 10.96.0.1 443/TCP 4m20s kube-system service/kube-dns ClusterIP 10.96.0.10 53/UDP,53/TCP,9153/TCP 4m8s kube-system service/metrics-server ClusterIP 10.98.18.100 443/TCP 4m2s

I use k0s btw ,and it is fantastic.

Here is link number 1 - Previous text "k0s"

So for example, in my own personal infra repos and for projects I do, Make orchestrates Pulumi, dnscontrol (Holy shit is that tool underrated), ansible, k0s/k0sctl (I run that distro), and all the kubernetes stuff.

I wasn’t able to run Kubernetes in NAS last time I tried it. https://github.com/k0sproject/k0s/issues/1184. As for public access you don’t want to do it for security reasons and instead rely on vpn. Tailscale and ZeroTier are easy to setup.

I prefer k0s, https://k0sproject.io/ .

actually that is not really true - i strongly urge you to try out http://k3s.io/ or https://k0sproject.io/

these are full-fledged, certified k8s distributions that run on raspberry pi as well as all the way in production.

https://www.youtube.com/results?search_query=raspberry+pi+k3...

You can check out our OSS https://github.com/orchest/orchest

The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/

First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!

Disclaimer: we're building something very similar and I'm curious about a couple of things.

One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.

Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.

How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.

Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!

For those who are curious, I'm one of the authors of https://github.com/orchest/orchest

Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).

If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest

You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation

We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest

For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.

Disclaimer: author of Orchest https://github.com/orchest/orchest