k3d | Little helper to run Rancher Lab 's k3s in Docker | Continuous Deployment library

It looks like Docker Desktop for Mac is using a 1.22+ version of Kubernetes which introduces a number of breaking changes specifically to the ingress-nginx controller apiVersion. This is causing issues with our local development cluster.

There are couple options:

1. Rolling back the Kubernetes version to something <1.22 in the development cluster.
2. Updating ingress-nginx and the development configuration to use >=1.22.

I'm trying to go with route 1. and downgrade the version to match our production cluster: v1.20.7 primarily because 1.22+ isn't available in Azure yet it looks like. It makes sense to me to have the development and production versions match.

That is my question: How do you change the version of Kuberentes that `docker-desktop uses?

If that can't be done, then I guess I'll just have to go with 2.

What've tried so far is simply following the kubectl documentation:

I have created a K3d cluster. I've Deployed a jenkins 2.319.1 controller inside, along with kubernetes plugin 1.31.1 (and git, pipeline and the like) The idea is to run both the controller and the agents in the same cluster. To do so I've configured a cloud like in this picture: [Cloud Configuration ][1] [1]: https://i.stack.imgur.com/u91Fr.png

(I've done several attempts with different combinations for the agents to connect to the controller. Finally I've stayed with JNLP - NO WEBSOCKET - although, anyway, the timeout I'm about to describe is common to both of them)

With the cloud configured and being able to spawn the agents, after many attempts I finally discovered that the job was hanging (and dying of timeout) in the execution inside a container step. Actions within the default "jnlp" container are ok but the moment you do something as trivial as: sh 'ls -l' inside another container the job dies after 30 seconds with the following log:

I am currently trying to move my calico based clusters to the new Dataplane V2, which is basically a managed Cilium offering. For local testing, I am running k3d with open source cilium installed, and created a set of NetworkPolicies (k8s native ones, not CiliumPolicies), which lock down the desired namespaces.

My current issue is, that when porting the same Policies on a GKE cluster (with DataPlane enabled), those same policies don't work.

As an example let's take a look into the connection between some app and a database:

I have a dictionary and a list:

I have the following skaffold yaml file:

I am trying to deploy two apps on a local k3d k8s cluster. Service-A exposes a REST endpoint that is consumed by Service-B. This is the configuration:

Service-A

When working with matplotlib widgets and k3d to visualize 3D data, the requests are constructed such, that the graphical data is being packed into the HTTP header. This header size is limited in the underlying webserver used by jupyter server (tornado). If it is exceeded by the client request (e.g. visualizing a large plot), the following exception pops up in the log file of Jupyter:

I have two mac mini computers and I'm trying to create a K8s cluster using k3d. I can create a cluster on a single host very easily. However, I'm having trouble finding any guidance on creating a cluster that has multiple hosts (machines). Any ideas?

I have a website where I document a list of installed pythonic libraries.

For each library, I want to have available:

• The name of the library (obviously)
• A link to the documentation for the library (because documentation is useful)
• A brief description of the library (so people can quickly see what the library does)
• The currently installed version (to stop people asking me "Are you using version x.y?")

My current solution is to use the name as the text of a link, href'd to its documentation, and accept that the version & description are supplementary information, and can be made available to the user using a tool-tip - so they can sit in a title attribute

Example: