Notice: This repository has been moved to https://github.com/IBM/auto-kubeflow. Please use the new URL and this repository will be deleted soon.

Kubeflow, an end-to-end Machine Learning(ML) platform on kubernetes, provides components and features to compose ML pipelines. On Kubeflow website, it provides lots of documentation, including Geting Started, tutorials, deployment and etc, help you understand its components and features. However, when come to deployment on specific cloud provider, it usually involves kubernetes cluster creation, environment setup locally, deployment, configuration and etc. One mistake in these procedures may lead to a painful debugging, reconfiguration and even do-over from scratch.

In this repo, we focus on providing a convenient approach to deployment Kubeflow on IBM Cloud:

• Use Schematics/Terraform + Ansible to create a kubernete cluster on IBM Cloud, deploy multi-user kubeflow and integrate with AppID service as login mechanism.

It leverages the Schematics service on IBM Cloud to provision resources and kubernetes cluster service. Then it finishes the deployment and configuration with Ansible playbook.

Here is the summary of the contnets provided in this repo (more to come):

• Schematics terraform scripts for deploy kubeflow on IBM Kubernete Service using classic cluster.
• Schematics terraform scripts for deploy kubeflow on IBM Kubernete Service using VPC Gen 2 cluster
• Step by step tutorial of using Schematics service and resources on this repo to deploy a multi-user kubeflow cluster

Currently, the deployment is targeting kubeflow v1.3. The manifest used to deploy kubeflow is here: https://raw.githubusercontent.com/IBM/manifests/v1.3/distributions/kfdef/kfctl_ibm_multi_user.v1.3.0.yaml

For you to get start, please check out the tutorial here. It will guid you through the deployment process. Hopefully, you would be able to have a kubeflow cluster up and running with just a few clicks.