Kubeflow on Kubernetes Cluster

Kubeflow Pipelines are a new component of Kubeflow that can help you compose, deploy, and manage end-to-end (optionally hybrid) machine learning workflows. Because they are a useful component of Kubeflow, they give you a no lock-in way to advance from prototyping to production. Kubeflow Pipelines also support rapid and reliable experimentation, so users can try many ML techniques to identify what works best for their application.

Useful Links

• Getting Started Kubeflow Pipelines
• Introducing Kubeflow Composable

Kubeflow Version

• Kubeflow v0.4.1

Prerequisites

• Git
• Ksonnet
• Golang
• Kubernetes Cluster
• Kubectl

Components

• ambassador
• argo
• centraldashboard
• jupyterhub
• katib
• params.libsonnet
• pytorch-operator
• seldon
• spartakus
• tf-job-operator

Deployment

export KUBEFLOW_TAG=0.4.1
export NAMESPACE=kubeflow

git clone https://github.com/saidsef/kubeflow-on-k8s.git

cd kubeflow-on-k8s/

git submodule foreach git pull origin master

mkdir -p /mnt/{katib-mysql,kf-ml-data,kf-openvino,kf-minio}

kubectl apply -f ./deployment --namespace ${NAMESPACE}

ks apply default --namespace ${NAMESPACE} # append `--dry-run` for dry run

kubectl get all -n ${NAMESPACE}