---
title: "Install"
date: 2019-08-22T00:00:00-08:00
weight: 10
draft: false
---
In this chapter, we will install Kubeflow on Amazon EKS cluster. If you don't have an EKS cluster, please follow instructions from [getting started guide](/020_prerequisites) and then launch your EKS cluster using [eksctl](/030_eksctl) chapter
### Ensure you have OIDC provider attached to the cluster
First check if the EKS cluster has OIDC provider attched by running the command:
```
eksctl get cluster eksworkshop-eksctl -o json | jq -r .[0].Identity
```
It the output of the previous command is blank, create an [OIDC provider](https://docs.aws.amazon.com/eks/latest/userguide/enable-iam-roles-for-service-accounts.html) and associate it with for your EKS cluster with the following command:
```
eksctl utils associate-iam-oidc-provider --cluster ${CLUSTER_NAME} \
--region ${CLUSTER_REGION} --approve
```
### Increase cluster size
We need more resources for completing the Kubeflow chapter of the EKS Workshop.
First, we'll increase the size of our cluster to 8 nodes
```
export NODEGROUP_NAME=$(eksctl get nodegroups --cluster eksworkshop-eksctl -o json | jq -r '.[0].Name')
eksctl scale nodegroup --cluster eksworkshop-eksctl --name $NODEGROUP_NAME --nodes 8 --nodes-max 8
```
{{% notice info %}}
Scaling the nodegroup will take 2 - 3 minutes.
{{% /notice %}}
### Install Kubeflow on Amazon EKS
#### Install Kustomize
Download 3.2.0 release of `kustomize`. This binary will allow you to install Kubeflow on Amazon EKS
{{% notice info %}}
Warning: Kubeflow is not compatible with the latest versions of of kustomize 4.x. This is due to changes in the order that resources are sorted and printed. Please see [kubernetes-sigs/kustomize#3794](https://github.com/kubernetes-sigs/kustomize/issues/3794) and [kubeflow/manifests#1797](https://github.com/kubeflow/manifests/issues/1797). We know that this is not ideal and are working with the upstream kustomize team to add support for the latest versions of kustomize as soon as we can.
{{% /notice %}}
```
curl --silent --location "https://github.com/kubernetes-sigs/kustomize/releases/download/v3.2.0/kustomize_3.2.0_linux_amd64" -o /tmp/kustomize
sudo chmod +x /tmp/kustomize && sudo mv -v /tmp/kustomize /usr/local/bin
```
#### Clone the repository
Clone the [`awslabs/kubeflow-manifests`](https://github.com/awslabs/kubeflow-manifests) and the [`kubeflow/manifests`](https://github.com/kubeflow/manifests) repositories and check out the release branches of your choosing.
Substitute the value for `KUBEFLOW_RELEASE_VERSION`(e.g. v1.5.1) and `AWS_RELEASE_VERSION`(e.g. v1.5.1-aws-b1.0.0) with the tag or branch you want to use below. Read more about [releases and versioning](https://awslabs.github.io/kubeflow-manifests/release-v1.5.1-aws-b1.0.0/docs/about/releases/) if you are unsure about what these values should be.
```bash
export KUBEFLOW_RELEASE_VERSION=v1.5.1
export AWS_RELEASE_VERSION=v1.5.1-aws-b1.0.0
git clone https://github.com/awslabs/kubeflow-manifests.git && cd kubeflow-manifests
git checkout ${AWS_RELEASE_VERSION}
git clone --branch ${KUBEFLOW_RELEASE_VERSION} https://github.com/kubeflow/manifests.git upstream
```
#### Build Manifests and install Kubeflow
There two options for installing Kubeflow official components and common services with kustomize.
1. Single-command installation of all components under `apps` and `common`
2. Multi-command, individual components installation for `apps` and `common`
Option 1 targets ease of deployment for end users. This lab we will use this option.\
Option 2 targets customization and ability to pick and choose individual components.
> Warning: In both options, we use a default email (`user@example.com`) and password (`12341234`). For any production Kubeflow deployment, you should change the default password by following [the relevant section](#change-default-user-password).
---
**NOTE**
`kubectl apply` commands may fail on the first try. This is inherent in how Kubernetes and `kubectl` work (e.g., CR must be created after CRD becomes ready). The solution is to re-run the command until it succeeds. For the single-line command, we have included a bash one-liner to retry the command.
---
#### Install with a single command
You can install all Kubeflow official components (residing under `apps`) and all common services (residing under `common`) using the following command:
```sh
while ! kustomize build deployments/vanilla | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 30; done
```
Run below command to check the status
```
kubectl -n kubeflow get all
```
{{% notice info %}}
Installing Kubeflow and its toolset may take 2 - 3 minutes. Few pods may initially give Error or CrashLoopBackOff status. Give it some time, they will auto-heal and will come to Running state
{{% /notice %}}
{{%expand "Expand here to see the output" %}}
```
$ kubectl -n kubeflow get all
NAME READY STATUS RESTARTS AGE
pod/admission-webhook-deployment-7df7558c67-t6nkd 1/1 Running 0 26m
pod/cache-server-5bdbd59959-hn54n 2/2 Running 0 26m
pod/centraldashboard-79f489b55-q7j4b 2/2 Running 0 26m
pod/jupyter-web-app-deployment-7cd59c5c95-rdqpf 1/1 Running 0 26m
pod/katib-controller-58ddb4b856-72jq7 1/1 Running 0 26m
pod/katib-db-manager-d77c6757f-fmwzk 1/1 Running 0 26m
pod/katib-mysql-7894994f88-fclb8 1/1 Running 0 26m
pod/katib-ui-f787b9d88-vbshr 1/1 Running 0 26m
pod/kfserving-controller-manager-0 2/2 Running 0 26m
pod/kfserving-models-web-app-7884f597cf-n7cn8 2/2 Running 0 26m
pod/kserve-models-web-app-5c64c8d8bb-br6rp 2/2 Running 0 26m
pod/kubeflow-pipelines-profile-controller-84bcbdb899-65wsn 1/1 Running 0 26m
pod/metacontroller-0 1/1 Running 0 26m
pod/metadata-envoy-deployment-86d856fc6-mr95n 1/1 Running 0 26m
pod/metadata-grpc-deployment-f8d68f687-rhfvk 2/2 Running 3 26m
pod/metadata-writer-d7ff8d4bc-txdn8 2/2 Running 0 26m
pod/minio-5b65df66c9-lkw4f 2/2 Running 0 26m
pod/ml-pipeline-7499f55b46-w7w8l 2/2 Running 1 26m
pod/ml-pipeline-persistenceagent-7bf47b869c-nd8tb 2/2 Running 0 26m
pod/ml-pipeline-scheduledworkflow-565fd7846-n52x9 2/2 Running 0 26m
pod/ml-pipeline-ui-77477f77dc-4gjzg 2/2 Running 0 26m
pod/ml-pipeline-viewer-crd-68bcdc87f9-9d72b 2/2 Running 1 26m
pod/ml-pipeline-visualizationserver-7bc59978d-sdh84 2/2 Running 0 26m
pod/mysql-f7b9b7dd4-5mgtw 2/2 Running 0 26m
pod/notebook-controller-deployment-7474fbff66-lvftm 2/2 Running 1 26m
pod/profiles-deployment-5cc86bc965-zktbg 3/3 Running 2 26m
pod/tensorboard-controller-controller-manager-5cbddb7fb5-j6hkw 3/3 Running 2 26m
pod/tensorboards-web-app-deployment-7c5db448d7-jqr58 1/1 Running 0 26m
pod/training-operator-6bfc7b8d86-grfm8 1/1 Running 0 26m
pod/volumes-web-app-deployment-87484c848-lggnh 1/1 Running 0 26m
pod/workflow-controller-5cb67bb9db-ph2q9 2/2 Running 1 26m
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/admission-webhook-service ClusterIP 10.100.204.94 443/TCP 26m
service/cache-server ClusterIP 10.100.124.41 443/TCP 26m
service/centraldashboard ClusterIP 10.100.26.238 80/TCP 26m
service/jupyter-web-app-service ClusterIP 10.100.50.251 80/TCP 26m
service/katib-controller ClusterIP 10.100.132.8 443/TCP,8080/TCP 26m
service/katib-db-manager ClusterIP 10.100.231.114 6789/TCP 26m
service/katib-mysql ClusterIP 10.100.4.87 3306/TCP 26m
service/katib-ui ClusterIP 10.100.44.197 80/TCP 26m
service/kfserving-controller-manager-metrics-service ClusterIP 10.100.146.200 8443/TCP 26m
service/kfserving-controller-manager-service ClusterIP 10.100.75.226 443/TCP 26m
service/kfserving-models-web-app ClusterIP 10.100.193.57 80/TCP 26m
service/kfserving-webhook-server-service ClusterIP 10.100.115.110 443/TCP 26m
service/kserve-models-web-app ClusterIP 10.100.160.240 80/TCP 26m
service/kubeflow-pipelines-profile-controller ClusterIP 10.100.44.161 80/TCP 26m
service/metadata-envoy-service ClusterIP 10.100.111.207 9090/TCP 26m
service/metadata-grpc-service ClusterIP 10.100.56.101 8080/TCP 26m
service/minio-service ClusterIP 10.100.225.255 9000/TCP 26m
service/ml-pipeline ClusterIP 10.100.36.226 8888/TCP,8887/TCP 26m
service/ml-pipeline-ui ClusterIP 10.100.192.99 80/TCP 26m
service/ml-pipeline-visualizationserver ClusterIP 10.100.70.121 8888/TCP 26m
service/mysql ClusterIP 10.100.51.87 3306/TCP 26m
service/notebook-controller-service ClusterIP 10.100.193.94 443/TCP 26m
service/profiles-kfam ClusterIP 10.100.122.221 8081/TCP 26m
service/tensorboard-controller-controller-manager-metrics-service ClusterIP 10.100.102.234 8443/TCP 26m
service/tensorboards-web-app-service ClusterIP 10.100.138.222 80/TCP 26m
service/training-operator ClusterIP 10.100.223.54 8080/TCP 26m
service/volumes-web-app-service ClusterIP 10.100.95.178 80/TCP 26m
service/workflow-controller-metrics ClusterIP 10.100.176.155 9090/TCP 26m
NAME READY UP-TO-DATE AVAILABLE AGE
deployment.apps/admission-webhook-deployment 1/1 1 1 26m
deployment.apps/cache-server 1/1 1 1 26m
deployment.apps/centraldashboard 1/1 1 1 26m
deployment.apps/jupyter-web-app-deployment 1/1 1 1 26m
deployment.apps/katib-controller 1/1 1 1 26m
deployment.apps/katib-db-manager 1/1 1 1 26m
deployment.apps/katib-mysql 1/1 1 1 26m
deployment.apps/katib-ui 1/1 1 1 26m
deployment.apps/kfserving-models-web-app 1/1 1 1 26m
deployment.apps/kserve-models-web-app 1/1 1 1 26m
deployment.apps/kubeflow-pipelines-profile-controller 1/1 1 1 26m
deployment.apps/metadata-envoy-deployment 1/1 1 1 26m
deployment.apps/metadata-grpc-deployment 1/1 1 1 26m
deployment.apps/metadata-writer 1/1 1 1 26m
deployment.apps/minio 1/1 1 1 26m
deployment.apps/ml-pipeline 1/1 1 1 26m
deployment.apps/ml-pipeline-persistenceagent 1/1 1 1 26m
deployment.apps/ml-pipeline-scheduledworkflow 1/1 1 1 26m
deployment.apps/ml-pipeline-ui 1/1 1 1 26m
deployment.apps/ml-pipeline-viewer-crd 1/1 1 1 26m
deployment.apps/ml-pipeline-visualizationserver 1/1 1 1 26m
deployment.apps/mysql 1/1 1 1 26m
deployment.apps/notebook-controller-deployment 1/1 1 1 26m
deployment.apps/profiles-deployment 1/1 1 1 26m
deployment.apps/tensorboard-controller-controller-manager 1/1 1 1 26m
deployment.apps/tensorboards-web-app-deployment 1/1 1 1 26m
deployment.apps/training-operator 1/1 1 1 26m
deployment.apps/volumes-web-app-deployment 1/1 1 1 26m
deployment.apps/workflow-controller 1/1 1 1 26m
NAME DESIRED CURRENT READY AGE
replicaset.apps/admission-webhook-deployment-7df7558c67 1 1 1 26m
replicaset.apps/cache-server-5bdbd59959 1 1 1 26m
replicaset.apps/centraldashboard-79f489b55 1 1 1 26m
replicaset.apps/jupyter-web-app-deployment-7cd59c5c95 1 1 1 26m
replicaset.apps/katib-controller-58ddb4b856 1 1 1 26m
replicaset.apps/katib-db-manager-d77c6757f 1 1 1 26m
replicaset.apps/katib-mysql-7894994f88 1 1 1 26m
replicaset.apps/katib-ui-f787b9d88 1 1 1 26m
replicaset.apps/kfserving-models-web-app-7884f597cf 1 1 1 26m
replicaset.apps/kserve-models-web-app-5c64c8d8bb 1 1 1 26m
replicaset.apps/kubeflow-pipelines-profile-controller-84bcbdb899 1 1 1 26m
replicaset.apps/metadata-envoy-deployment-86d856fc6 1 1 1 26m
replicaset.apps/metadata-grpc-deployment-f8d68f687 1 1 1 26m
replicaset.apps/metadata-writer-d7ff8d4bc 1 1 1 26m
replicaset.apps/minio-5b65df66c9 1 1 1 26m
replicaset.apps/ml-pipeline-7499f55b46 1 1 1 26m
replicaset.apps/ml-pipeline-persistenceagent-7bf47b869c 1 1 1 26m
replicaset.apps/ml-pipeline-scheduledworkflow-565fd7846 1 1 1 26m
replicaset.apps/ml-pipeline-ui-77477f77dc 1 1 1 26m
replicaset.apps/ml-pipeline-viewer-crd-68bcdc87f9 1 1 1 26m
replicaset.apps/ml-pipeline-visualizationserver-7bc59978d 1 1 1 26m
replicaset.apps/mysql-f7b9b7dd4 1 1 1 26m
replicaset.apps/notebook-controller-deployment-7474fbff66 1 1 1 26m
replicaset.apps/profiles-deployment-5cc86bc965 1 1 1 26m
replicaset.apps/tensorboard-controller-controller-manager-5cbddb7fb5 1 1 1 26m
replicaset.apps/tensorboards-web-app-deployment-7c5db448d7 1 1 1 26m
replicaset.apps/training-operator-6bfc7b8d86 1 1 1 26m
replicaset.apps/volumes-web-app-deployment-87484c848 1 1 1 26m
replicaset.apps/workflow-controller-5cb67bb9db 1 1 1 26m
NAME READY AGE
statefulset.apps/kfserving-controller-manager 1/1 26m
statefulset.apps/metacontroller 1/1 26m
NAME SCHEDULE SUSPEND ACTIVE LAST SCHEDULE AGE
cronjob.batch/aws-kubeflow-telemetry 0 0 * * * False 0 26m
```
{{% /expand %}}
Once everything is installed successfully, you can access the Kubeflow Central Dashboard [by logging into your cluster](#connect-to-your-kubeflow-cluster).
You can now start experimenting and running your end-to-end ML workflows with Kubeflow!