Please see How to Minikube
$ minikube start --cpus 4 --memory 8192If you want to use the vm driver with hyperkit
$ minikube start --vm-driver=hyperkit --cpus 4 --memory 8192$ git clone [email protected]:google/kubeflow.git
$ cd kubeflow
$ kubectl apply -f components/ -R
# Wait until all pod are getting running state. (6-7 mins)Get the URL for the notebook.
$ minikube service tf-hub-lb --urlor access JupyterHub on http://localhost:8000 by
$ kubectl port-forward tf-hub-0 8000:8000input any username and password will login
Image : jupyter/scipy-notebook (jupyterhub version MUST be at least 0.8.1)
CPU: 200m (could be more)
Memory: 256Mi (could be more)
Extra Resource Limits: (leave it empty)
than press the Spawn button
##Tensorflow wit notebook in JupyterHub
Image : gcr.io/kubeflow/tensorflow-notebook-cpu
CPU: 300m (almost run out of quota in 4 CPU minikube)
Memory: 1.5Gi (could be more)
Extra Resource Limits: (leave it empty)
than press the Spawn button
You may get this message, python3.5 still works. but we are in 3.6 wait until google fixed this issue. We could just ignore.
/home/raju/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6See tensorflow/tensorflow#14182
$ kubectl describe node 
Hi guys. FYI I remember that I switched to a different approach which turned successful based on MicroK8s and multipass prior to installing KubeFlow. Here are a few links I used: https://itnext.io/microk8s-on-macos-98f1de3aa63e , https://microk8s.io/docs/install-multipass https://www.youtube.com/watch?v=KPEGKKNB63Q
Once you have installed ubuntu server VM's, it's pretty easy and the youtube video I added above is crystal clear. Just a small remark : running KF is very heavy in terms of resources (deployed around 40 pods just to run KF) As far as I remember when I deployed it locally on my macbook pro (4core 16Gb Ram) my laptop was freezing so much that the only thing I managed to do was browsing the KF web UI and printing the k8s objects in my terminal :D If you want to achieve advanced stuff with KF like ML pipelines... I would advise you to go to the cloud and provision either VMs (and of course manually install k8s through kubeadm or even microk8s) or AKS/GKE (unless you have machines at home you can use to build your own cluster... if so drop me a mail as I have already done it and I can share some docs tips and tricks)