CODEWITHSUNDEEP
google-cloud-platformkubeflowkubeflow-pipelines
Eli
Eli

Reputation: 123

Kubeflow deployment on GCP

I have been reading for few weeks for different approaches for ML in production. I decided to test Kubeflow and I decided to test it on GCP. I started to deploy Kubeflow on GCP using the guiidline on official kubeflow website(here https://www.kubeflow.org/docs/gke/). I run into a lot of issues and it was quit hard to fix them. I started to look into a better approach and I noticed that GCP AI platform now offers deploying Kubeflow pipelines with just few simple steps. (https://cloud.google.com/ai-platform/pipelines/docs/connecting-with-sdk.)

After easily setting up this, I had few question and doubts. If it is this much easy to set up and deploy Kubeflow why we have to go through such a cumbersome way as suggested in the kubeflow official website. Since creating Kubeflow pipeline on GCP means basically I am deploying Kubeflow on GCP, does that mean I can access other Kubeflow services like Katib?

Elnaz

Upvotes: 1

Views: 269

Answers (1)

Adam Leary
Adam Leary

Reputation: 111

The GCP managed service of Kubeflow Pipelines is just that. You won't have a lot of access to the cluster to make changes. I've deployed a Kubeflow cluster that can still reach the AI Hub as well.

I believe they have plans to expand what can be deployed in the AI Platform but if you don't want to wait, the self-deployment is possible (but not easy) IMO.

Upvotes: 0

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