如何使用 K8s on demand (GKE) 部署工作负载？

Question

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?

Answer 1

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.