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docs updated (#240)

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Akado2009 2018-09-24 15:07:27 -07:00 committed by k8s-ci-robot
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14
github_issue_summarization/01_setup_a_kubeflow_cluster.md
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@ -5,11 +5,24 @@ In this part, you will setup kubeflow on an existing kubernetes cluster.
## Requirements ## Requirements
* A kubernetes cluster * A kubernetes cluster
* To create a managed cluster run
```commandline
gcloud container clusters create kubeflow-examples-cluster
```
or use kubeadm: [docs](https://kubernetes.io/docs/setup/independent/create-cluster-kubeadm/)
* `kubectl` CLI (command line interface) pointing to the kubernetes cluster * `kubectl` CLI (command line interface) pointing to the kubernetes cluster
* Make sure that you can run `kubectl get nodes` from your terminal * Make sure that you can run `kubectl get nodes` from your terminal
successfully successfully
* The ksonnet CLI, v0.9.2 or higher: [ks](https://ksonnet.io/#get-started) * The ksonnet CLI, v0.9.2 or higher: [ks](https://ksonnet.io/#get-started)
* In case you want to install a particular version of ksonnet, you can run
```commandline
export KS_VER=ks_0.11.0_linux_amd64
wget -O /tmp/$KS_VER.tar.gz https://github.com/ksonnet/ksonnet/releases/download/v0.11.0/$KS_VER.tar.gz
mkdir -p ${HOME}/bin
tar -xvf /tmp/$KS_VER.tar.gz -C ${HOME}/bin
export PATH=$PATH:${HOME}/bin/$KS_VER
```
## Kubeflow setup ## Kubeflow setup
Refer to the [user Refer to the [user
@ -68,5 +81,6 @@ tf-job-operator-77776c8446-lpprm 1/1 Running 0
* We deployed the kubeflow-core component to our kubernetes cluster * We deployed the kubeflow-core component to our kubernetes cluster
* We created a disk for storing our training data * We created a disk for storing our training data
* We connected to JupyterHub and spawned a new Jupyter notebook * We connected to JupyterHub and spawned a new Jupyter notebook
* For additional details and playground visit [katacoda](https://www.katacoda.com/kubeflow/scenarios/deploying-github-issue-summarization)
*Next*: [Training the model](02_training_the_model.md) *Next*: [Training the model](02_training_the_model.md)