Clarified some things in the intro page for the MNIST notebook (#1731)

* Clarified some things in intro to the MNIST notebook.

* Added a link to the notebook itself.

* Removed item about testing in a local notebook

content/docs/gke/gcp-e2e.md

@@ -1,11 +1,13 @@
 +++
-title = "End-to-end Kubeflow on GCP"
-description = "Kubeflow on Google Cloud Platform"
+title = "Tutorial: End-to-end Kubeflow on GCP"
+description = "Training an MNIST model with Kubeflow on Google Cloud Platform"
 weight = 90
 +++
 
 This guide walks you through an end-to-end example of Kubeflow on Google
-Cloud Platform (GCP). By working through the guide, you learn
+Cloud Platform (GCP) using a Jupyter notebook, 
+[mnist_gcp.ipynb](https://github.com/kubeflow/examples/blob/master/mnist/mnist_gcp.ipynb). 
+By working through the notebook, you learn
 how to deploy Kubeflow on Kubernetes Engine (GKE), train an MNIST machine
 learning model for image classification, and use the model for online inference
 (also known as online prediction).
@@ -29,7 +31,6 @@ cloud resources.
 Here's a list of the primary GCP services that you use when following this
 guide:
 
-  * [Deployment Manager][deployment-manager]
   * [GKE][kubernetes-engine]
   * [Compute Engine][compute-engine]
   * [Container Registry][container-registry]
@@ -72,8 +73,6 @@ In summary:
 * Setting up [Kubeflow][kubeflow] in a [GKE][kubernetes-engine]
   cluster.
 
-* Testing the code locally using a [Jupyter notebook][jupyter-notebook].
-
 * Training the model:
 
   * Packaging a TensorFlow program in a [Kubernetes][kubernetes] container.
@@ -89,15 +88,18 @@ In summary:
 
 It's time to get started!
 
-## Run the MNIST tutorial on GCP
-
-1. Follow the [GCP instructions](/docs/gke/deploy/) to deploy Kubeflow with IAP.
+## Set up and run the MNIST tutorial on GCP
 
-1. Launch a Jupyter notebook.
+1. Follow the [GCP instructions](/docs/gke/deploy/) to deploy Kubeflow with 
+  Cloud Identity-Aware Proxy (IAP).
 
-   * The tutorial has been tested using the Jupyter Tensorflow 1.15 image
+1. Launch a Jupyter notebook in your Kubeflow cluster. See the guide to
+  [setting up your 
+  notebooks](/docs/notebooks/setup/#create-a-jupyter-notebook-server-and-add-a-notebook).
+  *Note:* This tutorial has been tested with the *Tensorflow 1.15 CPU* image
+  as the baseline image for the notebook.
 
-1. Launch a terminal in Jupyter and clone the Kubeflow examples repo.
+1. Launch a terminal in Jupyter and clone the Kubeflow examples repository:
 
    ```
    git clone https://github.com/kubeflow/examples.git git_kubeflow-examples
@@ -106,11 +108,11 @@ It's time to get started!
    * **Tip**: When you start a terminal in Jupyter, run the command `bash` to start
       a bash terminal which is much more friendly than the default shell.
 
-   * **Tip**: You can change the URL for your notebook from '/tree' to '/lab' to switch to using Jupyterlab.
+   * **Tip**: You can change the URL for your notebook from '/tree' to '/lab' to switch to using JupyterLab.
 
 1. Open the notebook `mnist/mnist_gcp.ipynb`.
 
-1. Follow the notebook to train and deploy MNIST on Kubeflow.
+1. Follow the instructions in the notebook to train and deploy MNIST on Kubeflow.
 
 
 [mnist-data]: http://yann.lecun.com/exdb/mnist/index.html
@@ -120,48 +122,9 @@ It's time to get started!
 [tf-serving]: https://www.tensorflow.org/serving/
 
 [kubernetes]: https://kubernetes.io/
-[kubectl]: https://kubernetes.io/docs/reference/kubectl/kubectl/
-[docker]: https://www.docker.com/
-
 [kubernetes-engine]: https://cloud.google.com/kubernetes-engine/
 [container-registry]: https://cloud.google.com/container-registry/
 [cloud-storage]: https://cloud.google.com/storage/
-[deployment-manager]: https://cloud.google.com/deployment-manager/
 [compute-engine]: https://cloud.google.com/compute/
-[billing-guide]: https://cloud.google.com/billing/docs/how-to/modify-project
-[regions-zones]: https://cloud.google.com/compute/docs/regions-zones/
-[iap]: https://cloud.google.com/iap/
-[dns]: https://cloud.google.com/dns/docs/
-
 [cloud-sdk]: https://cloud.google.com/sdk/docs/
-[gcloud]: https://cloud.google.com/sdk/gcloud/
-[gcp-project-id]: https://cloud.google.com/resource-manager/docs/creating-managing-projects#identifying_projects
-[gcp-locations]: https://cloud.google.com/about/locations/
-[gcp-console]: https://console.cloud.google.com/cloud-resource-manager
-[gcp-console-kubernetes-engine]: https://console.cloud.google.com/kubernetes
-[gcp-console-workloads]: https://console.cloud.google.com/kubernetes/workload
-[gcp-console-storage]: https://console.cloud.google.com/storage
-[gcp-console-consent]: https://console.cloud.google.com/apis/credentials/consent
-[gcp-console-credentials]: https://console.cloud.google.com/apis/credentials
-[gcp-console-deployment-manager]: https://console.cloud.google.com/dm/
-[gcp-console-compute-engine]: https://console.cloud.google.com/compute/
-[gcp-console-services]: https://console.cloud.google.com/kubernetes/discovery
-[cr-tf-models]: https://console.cloud.google.com/gcr/images/tensorflow/GLOBAL/models
-
-[cloud-shell]: https://cloud.google.com/sdk/docs/interactive-gcloud
-[gcloud-container-clusters-create]: https://cloud.google.com/sdk/gcloud/reference/container/clusters/create
-[gcp-machine-types]: https://cloud.google.com/compute/docs/machine-types
-[gcp-service-account]: https://cloud.google.com/iam/docs/understanding-service-accounts
-[gcp-container-registry]: https://console.cloud.google.com/gcr
-[gsutil-mb]: https://cloud.google.com/storage/docs/gsutil/commands/mb
-[gsutil-acl-ch]: https://cloud.google.com/storage/docs/gsutil/commands/acl#ch
-
-[flask]: http://flask.pocoo.org/
-
 [kubeflow]: https://www.kubeflow.org/
-
-[tf-training]: /docs/components/tftraining/
-
-[jupyter-notebook]: https://jupyter-notebook.readthedocs.io
-[kubeflow-jupyter]: /docs/notebooks/
-[jupyter-nbviewer]: https://jupyter-notebook.readthedocs.io/en/latest/notebook.html#notebook-user-interface