Deploying with minikube on a single node

How to deploy Kubeflow with minikube on a single node on Linux

This guide covers the installation of minikube and Kubeflow in a single-node Ubuntu system. minikube provides a single-node Kubernetes cluster, making it easy to learn and and develop for Kubernetes.

The guide covers the following topics:

installation of docker-community edition (docker-ce), kubectl, and Minikube
installation of Kubeflow
launch of Kubeflow central dashboard
execution of an MNIST on-prem notebook

Prerequisites

Ubuntu 18 machine with min 8 cores, 16GB RAM, 250GB storage
Root privileges on a Ubuntu machine

Installation of Docker CE, kubectl, and Minikube

Minikube provides a no-driver mode based on Docker without requiring a hypervisor.

Install Docker CE

Run the following commands to install Docker CE:

apt-get update
apt-get install -y apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | apt-key add -
add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"
apt-get update
apt-get install docker-ce docker-ce-cli containerd.io

To verify your installation, run:

docker run hello-world

The expected output looks like this:

Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
1b930d010525: Pull complete                                                                                        Digest: sha256:fc6a51919cfeb2e6763f62b6d9e8815acbf7cd2e476ea353743570610737b752
Status: Downloaded newer image for hello-world:latest

Hello from Docker!
This message shows that your installation appears to be working correctly.

Install kubectl

kubectl is a Kubernetes command-line tool that allows you to run commands against Kubernetes clusters. The following instructions install version 1.15 of kubectl. If you are looking for a specific version, see the kubectl installation guide.

curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.15.0/bin/linux/amd64/kubectl
chmod +x ./kubectl
mv ./kubectl /usr/local/bin/kubectl

Install minikube

Run the command lines below to install the latest version of minikube. If you are looking for a specific version, refer to the Kubernetes: minikube releases page.

 curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
 sudo install minikube-linux-amd64 /usr/local/bin/minikube

Start minikube

You can launch minikune with the minikube start command. For example, if you want to specify 6 CPUs, 12288 memory, 120G disk size:

minikube start --cpus 6 --memory 12288 --disk-size=120g --extra-config=apiserver.service-account-issuer=api --extra-config=apiserver.service-account-signing-key-file=/var/lib/minikube/certs/apiserver.key --extra-config=apiserver.service-account-api-audiences=api

Installation of Kubeflow

Follow these steps to install Kubeflow:

Download the kfctl v1.2.0 release from the Kubeflow releases page.
Extract the zipped TAR file:
```
tar -xvf {KFCTL_TAR_GZ_FILE}
```
where {KFCTL_TAR_GZ_FILE} should be replaced with the name of the kfctl release file you have just downloaded.

Run the commands below to set up and deploy Kubeflow. One of them includes an optional command to add the binary kfctl to your path. If you don’t add the binary to your path, you must use the full path to the kfctl binary each time you run it.

# The following command is optional making the kfctl binary easier to use.
export PATH=$PATH:<path to where kfctl was unpacked>

# Set KF_NAME to the name of your Kubeflow deployment. This also becomes the
# name of the directory containing your configuration.
# For example, your deployment name can be 'my-kubeflow' or 'kf-test'.
export KF_NAME=<your choice of name for the Kubeflow deployment>

# Set the path to the base directory where you want to store one or more 
# Kubeflow deployments. For example, /opt/.
# Then set the Kubeflow application directory for this deployment.
export BASE_DIR=<path to a base directory>
export KF_DIR=${BASE_DIR}/${KF_NAME}

# Set the configuration file to use, such as the file specified below:
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml"

# Generate and deploy Kubeflow:
mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

${KF_NAME} - The name of your Kubeflow deployment. If you want a custom deployment name, specify that name here. For example, my-kubeflow or kf-test. The value of KF_NAME must consist of lower case alphanumeric characters or ‘-’, and must start and end with an alphanumeric character. The value of this variable cannot be greater than 25 characters. It must contain just a name, not a directory path. This value also becomes the name of the directory where your Kubeflow configurations are stored, that is, the Kubeflow application directory.
${KF_DIR} - The full path to your Kubeflow application directory.

The following example installs Kubeflow v1.0.0 under the /root/kubeflow/v1.0 directory:

mkdir -p /root/kubeflow/v1.0
cd /root/kubeflow/v1.0
wget https://github.com/kubeflow/kfctl/releases/download/v1.0/kfctl_v1.0-0-g94c35cf_linux.tar.gz

tar -xvf kfctl_v1.0-0-g94c35cf_linux.tar.gz			
export PATH=$PATH:/root/kubeflow/v1.0
export KF_NAME=my-kubeflow
export BASE_DIR=/root/kubeflow/v1.0
export KF_DIR=${BASE_DIR}/${KF_NAME}
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml" 

mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

After the installation, run the following command to check whether all the pods are in running status:

kubectl get pod -n kubeflow

It may take a few minutes to reach full running status.

Expected output:

NAME                                                           READY   STATUS      RESTARTS   AGE
admission-webhook-bootstrap-stateful-set-0                     1/1     Running     0          10m
admission-webhook-deployment-64cb96ddbf-w7ptd                  1/1     Running     0        9m33s
application-controller-stateful-set-0                          1/1     Running     0          13m
argo-ui-778676df64-kjw6s                                       1/1     Running     0          10m
centraldashboard-7dd7dd685d-hvll8                              1/1     Running     0          10m
jupyter-web-app-deployment-89789fd5-cjkwf                      1/1     Running     0          10m
katib-controller-6b789b6cb5-kgzv8                              1/1     Running     1          10m
katib-db-manager-64f548b47c-sszv9                              1/1     Running     3        9m59s
katib-mysql-57884cb488-d4qt2                                   1/1     Running     0        9m59s
katib-ui-5c5cc6bd77-2kqvc                                      1/1     Running     0        9m59s
kfserving-controller-manager-0                                 2/2     Running     1          10m
metacontroller-0                                               1/1     Running     0          10m
metadata-db-76c9f78f77-7r8vb                                   0/1     Running     1          10m
metadata-deployment-674fdd976b-hzmzx                           0/1     Running     0          10m
metadata-envoy-deployment-5688989bd6-rqtrk                     1/1     Running     0          10m
metadata-grpc-deployment-5579bdc87b-xx9fk                      1/1     Running     6          10m
metadata-ui-9b8cd699d-scs9d                                    1/1     Running     0          10m
minio-755ff748b-lxsb6                                          1/1     Running     0        9m55s
ml-pipeline-79b4f85cbc-8tjff                                   1/1     Running     0        9m55s
ml-pipeline-ml-pipeline-visualizationserver-5fdffdc5bf-zsngc   1/1     Running     0        9m42s
ml-pipeline-persistenceagent-645cb66874-b465h                  1/1     Running     0        9m54s
ml-pipeline-scheduledworkflow-6c978b6b85-cffng                 1/1     Running     0        9m42s
ml-pipeline-ui-6995b7bccf-s642q                                1/1     Running     0        9m45s
ml-pipeline-viewer-controller-deployment-8554dc7b9f-vgw9r      1/1     Running     0        9m44s
mysql-598bc897dc-lsmqc                                         1/1     Running     0        9m54s
notebook-controller-deployment-7db57b9ccf-vw9vw                1/1     Running     0          10m
profiles-deployment-5d87dd4f87-7gfrj                           2/2     Running     0        9m41s
pytorch-operator-5fd5f94bdd-kfgvp                              1/1     Running     2          10m
seldon-controller-manager-679fc777cd-4n229                     1/1     Running     0        9m39s
spark-operatorcrd-cleanup-kxr7g                                0/2     Completed   0          10m
spark-operatorsparkoperator-c7b64b87f-cfptj                    1/1     Running     0          10m
spartakus-volunteer-6b767c8d6-4v6hc                            1/1     Running     0          10m
tensorboard-6544748d94-8ctk8                                   1/1     Running     0          10m
tf-job-operator-7d7c8fb8bb-xz5pw                               1/1     Running     1          10m
workflow-controller-945c84565-57c72                            1/1     Running     0          10m

Launch of Kubeflow central dashboard

You can access the Kubeflow dashboard using the istio-ingressgateway service. To check your settings for istio-ingressgateway, execute the following commands:

export INGRESS_HOST=$(minikube ip)
export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].nodePort}')

Then you can access the Kubeflow dashboard in a web browser:

http://<INGRESS_HOST>:<INGRESS_PORT>

Running the MNIST on-prem Jupyter notebook

The MNIST on-prem notebook builds a Docker image, launches a TFJob to train a model, and creates an InferenceService (KFServing) to deploy the trained model.

Prerequisites

Step 1: Set up Python environment

You need Python 3.5 or later:

apt-get update; apt-get install -y wget bzip2
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Re-open your current shell.

Create a Python 3.7 environment named mlpipeline (or any name that you prefer):

conda create --name mlpipeline python=3.7
conda init
conda activate mlpipeline

Step 2: Install Jupyter Notebooks

Run the following command to install Jupyter Notebooks:

pip install --upgrade pip
pip install jupyter

You can find the full instructions in the Jupyter documentation.

Step 3: Create a Docker ID

In order to build Docker images from your notebook, you need a Docker registry to store the images. If you don’t have a Docker ID, follow the Docker documentation to create one.

Step 4: Create a namespace to run the MNIST on-prem notebook

The following commands create a namespace called mnist. You can use any name you like:

kubectl create ns mnist
kubectl label namespace mnist serving.kubeflow.org/inferenceservice=enabled

Step 5: Download the MNIST on-prem notebook

Run the following commands to download the notebook:

cd /root/kubeflow
git clone https://github.com/kubeflow/fairing.git

Launch Jupyter Notebook

Run the following commands to launch a Jupyter Notebooks server:

cd /root/kubeflow/fairing/examples/mnist
conda activate mlpipeline
docker login
jupyter notebook --allow-root

Your output should be similar to this:

[I 21:17:37.473 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[I 21:17:37.784 NotebookApp] Serving notebooks from local directory: /root/kubeflow/fairing/examples/mnist
[I 21:17:37.784 NotebookApp] The Jupyter Notebook is running at:
[I 21:17:37.784 NotebookApp] http://localhost:8888/?token=06cd43860cb7cb214bba30028d4b93f9f1acb08e3121ee13
[I 21:17:37.784 NotebookApp]  or http://127.0.0.1:8888/?token=06cd43860cb7cb214bba30028d4b93f9f1acb08e3121ee13
[I 21:17:37.784 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 21:17:37.789 NotebookApp] No web browser found: could not locate runnable browser.
[C 21:17:37.789 NotebookApp]

    To access the notebook, open this file in a browser:
        file:///root/.local/share/jupyter/runtime/nbserver-16796-open.html
    Or copy and paste one of these URLs:
        http://localhost:8888/?token=06cd43860cb7cb214bba30028d4b93f9f1acb08e3121ee13
     or http://127.0.0.1:8888/?token=06cd43860cb7cb214bba30028d4b93f9f1acb08e3121ee13

See the hints and tips section to learn how to access the notebook user interface (UI) remotely.

Execute MNIST on-prem notebook

After launching Jupyter Notebooks, click mnist_e2e_on_prem.ipynb to open the notebook. Take the following steps before running the notebook:

Under Configure The Docker Registry For Kubeflow Fairing, modify the cell to use your Docker ID and namespace created above.
```
   DOCKER_REGISTRY = 'index.docker.io/<your Docker ID>'
   my_namespace = 'mnist'
```
The MNIST on-prem notebook assumes that an NFS server is available or an existing persistent volume (PV) and persistent volume claim (PVC) is created. The NFS server is not installed with Minikube. If you don’t have an NFS server and don’t have any existing PV and PVC, you can instruct the notebook to create a PV and PVC by making the following change:

Under Create PV/PVC to Store The Exported Model:
1. In the first cell, comment out nfs_server and nfs_path:
```
# nfs_server = '172.16.189.69'
# nfs_path = '/opt/kubeflow/data/mnist'
pv_name = 'mnist-e2e-pv'
pvc_name = 'mnist-e2e-pvc'
```
1. In the second cell, change the nfs section to hostPath:
```
nfs:
  path: {nfs_path}
  server: {nfs_server}
```
  to
```
hostPath:
  path: /mnt/data
```

Hints and tips

Access a Jupyter Notebook UI remotely

If your Jupyter Notebooks server is hosted in a Linux machine and you want to access a notebook UI from your Windows/Mac machine, you can use the following port forwarding technique:

General syntax for port forwarding from XXXX to YYYY:

ssh -N -f -L localhost:YYYY:localhost:XXXX remoteuser@remotehost

Below is an example of accessing a Jupyter notebook hosted in a Linux machine, remotehost.com (port 8888), in a Windows machine using port 8889.

In the Windows machine, issue the following command:
```
ssh -N -f -L localhost:8889:localhost:8888 root@remotehost.com
```
Enter a password when prompted to access remotehost.com
In the Windows machine, open a web browser and enter the following URL:
```
localhost:8889
```

TroubleShooting

AttributeError when create namespace object

When using Kubeflow Fairing to build a Docker image and launch a training job (part of the notebook), you may see the following error:

AttributeError: 'V1TFJob' object has no attribute 'openapi_types'

This is caused by an existing issue in Kubernetes client API.

To bypass this issue, install Kubernetes Client API version 10.0.1:

pip install kubernetes==10.0.1

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Last modified 20.04.2021: Apply Docs Restructure to `v1.2-branch` = update `v1.2-branch` to current `master` v2 (#2612) (4e2602bd)

You are viewing documentation for Kubeflow 1.2

Deploying with minikube on a single node

Prerequisites

Installation of Docker CE, kubectl, and Minikube

Install Docker CE

Install kubectl

Install minikube

Start minikube

Installation of Kubeflow

Launch of Kubeflow central dashboard

Running the MNIST on-prem Jupyter notebook

Prerequisites

Step 1: Set up Python environment

Step 2: Install Jupyter Notebooks

Step 3: Create a Docker ID

Step 4: Create a namespace to run the MNIST on-prem notebook

Step 5: Download the MNIST on-prem notebook

Launch Jupyter Notebook

Execute MNIST on-prem notebook

Hints and tips

Access a Jupyter Notebook UI remotely

TroubleShooting

AttributeError when create namespace object

Feedback