I want to run k8s-visualizer for Kubernetes in der Google Cloud Platform. Just found how to run it local.
How to run it in the Google Cloud Platform?
The k8s-visualizer is written in a way that it depends on the kubectl proxy and runs all Ajax calls against /api/.... It isn't ready to run on the cluser.
If you want to have it on your cluster, you'd have to fork the existing code and adjust all API calls slightly to hit the apiserver.
Once this is done, wrap everything into a container and deploy it into a Pod along with a service.
A good starting point are the open pull requests
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I need to deploy a GPU intensive task on GCP. I want to use a Node.js Docker image and within that container to run a Node.js server that listens to HTTP requests and runs a Python image processing script on-demand (every time that a new HTTP request is received containing the images to be processed). My understanding is that I need to deploy a load balancer in front of the K8s cluster that has a static public IP address which then builds/launches containers every time a new HTTP request comes in? And then destroy the container once processing is completed. Is container re-use not a concern? I never worked with K8s before and I want to understand how it works and after reading the GKE documentation this is how I imagine the architecture. What am I missing here?
runs a Python image processing script on-demand (every time that a new HTTP request is received containing the images to be processed)
This can be solved on Kubernetes, but it is not a very common kind of workload.
The project that support your problem best is Knative with its per-request auto-scaler. Google Cloud Run is the easiest way to use this. But if you want to run this within your own GKE cluster, you can enable it.
That said, you can also design your Node.js service to integrate with the Kubernetes API-server to create Jobs - but it is not a good design to have common workload talk to the API-server. It is better to use Knative or Google Cloud Run.
I'm building a test automation tool that needs to launch a set of tests, collect logs and results. My plan is to build container with necessary dependency for test framework and launch them in Kubernetes.
Is there any application that abstracts complexity of managing the pod lifecycle and provides a simple API to achieve this use-case preferably through API? Basically my test scheduler need to deploy a container in kubernetes, launch a test and collect log files at the end.
I already looked at Knative and kubeless - they seem to be complex and may over-complicate what I'm trying to do here.
Based on information you provided all I can recomend is kubernetes API itself.
You can create a pod with it, wait for it to finish and gather logs. If thats all you need, you don't need any other fancy applications. Here is a list of k8s client libraries.
If you don't want to use client libraries you can always use REST api.
If you are not sure how to use REST api, run kubectl commands with --v=10 flag for debug output where you can see all requests between kubectl and api-server as a reference guide.
Kubernetes also provided detailed documentation for k8s REST api.
Try looking at https://microk8s.io/, it was built for those purposes.
And you can talk to the API server via the rest API same as in every k8s cluster.
I'm kinda new to Kubernets and I think I understand the basics of the whole system but most of the stuff I have read was about how to use kubectl to start a service and deployment and stuff.
But in my use case I have this web API running (built in ASP.net core) that takes a request, does some processing and depending on the input data has to start a secondary process.
A Kubernetes job with restart policy OnFailure seemed to be the way to implement those secondary processes but I can't find any resources on how the web server can be used to start this job.
You can use Kubernetes API to create a Job(or any kubernetes resource) from your application running inside the cluster. You can either install kubectl inside your applications's container and call it from your application code or use a kubernetes client library(https://github.com/kubernetes-client/csharp) to talk to kubernetes API server.
See the following answer for more details:
Kubernetes - Finding out how many replicas there are in a service?
I have achieved vast amount of automation in terms of creating projects, creating kubernetes engine and other IaaS elements, by using GCP APIs from Python GCP Client.
But I am not very positive on deploying docker container workloads to the provisioned cluster. The GCP documents point to kubectl apply -f config.yaml, but this entails using command line tools by first switching to project etc...
This is exactly what I am trying to get away from. Is there a google API that lets us accomplish this?
And no, I do not want third party deployment automation tools for various reasons.
You can use Kubernetes client library to deploy workload programatically.
Here is some client for kubernetes:
Go client: client-go
Java client: kubernetes-client/java
Python client: kubernetes-client/python
Requirement - New Relic monitoring for an application running in pods as part of a kubernetes cluster.
I have installed Kube-state-metrics on my cluster and able to see kubernetes dashboard using newrelic insights.
Also, need to configure the Application monitoring for the same. Following https://blog.newrelic.com/2017/11/27/monitoring-application-performance-in-kubernetes/ for the same.
Have some questions for the same -
Can this be achieved using kube-state-metrics ?
Do I need to have separate yaml file for each pod containing license key?
Do I need to make changes in my application also or adding the information in spec will work?
Do I need to install Java agent in every pod? If yes, will it eat resources?
Somehow, Installation of application monitoring is becoming complex. Please explain the exact requirement of installation
You didn't mention your stack, you should follow instructions on their site for your language. Typically you just pull in their agent library and configure credentials to get started. You should not have a reason to tell your pods apart, so the agent credentials should be the same for all pods
Installing agents at infrastructure will let you have infrastructure data. So you'll get alerts if you're running out of memory/space/cpu and such. Infrastructure agent cannot possibly know about application data. If you want application performance data (apm) you need to install the agent at the application level too and you'll get data such as http request rates, error rates and response times if it's a webserver. You can also annotate current transaction with data which is all application specific. They have a bunch of client agents, see if there's one for your stack. For example all you need for a nodejs service is require('newrelic') at the top of your app and configuration