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[IBM] update out-dated links in the docs #2260

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[IBM] update out-dated links in the docs #2260

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update docs
adrian555 Oct 6, 2020
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Update content/en/docs/ibm/existing-cluster.md
adrian555 Oct 6, 2020
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Update content/en/docs/ibm/existing-cluster.md
adrian555 Oct 6, 2020
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10 changes: 4 additions & 6 deletions content/en/docs/ibm/existing-cluster.md
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Expand Up @@ -4,15 +4,13 @@ description = "Set up a cluster if you already have one"
weight = 5

+++
{{% alert title="Out of date" color="warning" %}}
This guide contains outdated information pertaining to Kubeflow 1.0. This guide
needs to be updated for Kubeflow 1.1.
{{% /alert %}}

## Initial Setup for Existing Cluster
## Initial setup for your existing cluster

Get the Kubeconfig file:

```shell
ibmcloud ks cluster config --cluster $CLUSTER_NAME
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```

From here on, please see [Install Kubeflow](/docs/ibm/install-kubeflow).
From here on, please see [Install Kubeflow](/docs/ibm/deploy/install-kubeflow).
9 changes: 2 additions & 7 deletions content/en/docs/ibm/iks-e2e.md
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Expand Up @@ -4,11 +4,6 @@ description = "Running Kubeflow using IBM Cloud Kubernetes Service (IKS)"
weight = 250

+++
{{% alert title="Out of date" color="warning" %}}
This guide contains outdated information pertaining to Kubeflow 1.0. This guide
needs to be updated for Kubeflow 1.1.
{{% /alert %}}

This is a guide for an end-to-end example of Kubeflow on [IBM Cloud Kubernetes Service (IKS)](https://cloud.ibm.com/docs/containers?topic=containers-getting-started). The core steps will be to take a base Tensorflow model, modify it for distributed training, serve the resulting model with TFServing, and deploy a web application that uses the trained model.

## Introduction
Expand Down Expand Up @@ -60,15 +55,15 @@ In summary:
* Packaging a Tensorflow program in a container.
* Submitting a Tensorflow training ([tf.train][tf-train]) job.
* Using the model for prediction (inference):
* Saving the trained model to [IBM Cloud Object Storage][ibm-cos)].
* Saving the trained model to [IBM Cloud Object Storage][ibm-cos].
* Using [Tensorflow Serving][tf-serving] to serve the model.
* Running the simple web app to send prediction request to the model and display the result.

It's time to get started!

## Run the MNIST Tutorial on IKS

1. Follow the [IKS instructions](/docs/ibm/install-kubeflow) to deploy Kubeflow.
1. Follow the [IKS instructions](/docs/ibm/deploy/install-kubeflow) to deploy Kubeflow.
2. Launch a Jupyter notebook.
* For IBM Cloud, the default NFS storage does not support some of the Python package installation. Therefore, you need to create the notebook with the setting `Don't use Persistent Storage for User's home` enabled.
* Due to the Notebook user permission issue, you need to use custom images that were working in the previous version.
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