Updates to KFP End-to-end tutorial (Issue #860)

content/docs/pipelines/tutorials/pipelines-tutorial.md

@@ -20,8 +20,7 @@ Google Cloud Platform (GCP) is a suite of cloud computing services running
 on Google infrastructure. The services include compute power, data storage,
 data analytics, and machine learning.
 
-The [Cloud SDK][cloud-sdk] is a set of tools that you can use to interact with
-GCP from the command line, including the `gcloud` command and others.
+The [Cloud Shell][cloud-shell] is a browser interface that provides command-line access to cloud resources that you can use to interact with GCP, including the `gcloud` command and others. 
 
 [Kubernetes Engine][kubernetes-engine] (GKE) is a managed service on GCP where
 you can deploy containerized applications. You describe the resources that your
@@ -72,28 +71,20 @@ Follow these steps to set up your GCP environment:
 1. Select or create a project on the [GCP Console][gcp-console].
 1. Make sure that billing is enabled for your project. See the guide to
   [modifying a project's billing settings][billing-guide].
-1. Install the [Cloud SDK][cloud-sdk].
+1. Grant users access to kubeflow with the `Owner` and `IAP-secured Web App User` roles:
+
+      ```
+      gcloud projects add-iam-policy-binding <PROJECT>
+      --member user:<EMAIL> --role <ROLE>
+      ```
+    For more details, see [Granting, changing, and revoking access to resources][granting-changing-revoking-access].
 
 Notes:
 
 * As you work through this tutorial, your project uses billable components of
   GCP. To minimise costs, follow the instructions to
   [clean up your GCP resources](#cleanup) when you've finished with them.
-* This guide assumes you want to manage your GCP environment on your own server
-  rather than in the [Cloud Shell][cloud-shell] environment. If you choose to
-  use the Cloud Shell, some of the components are pre-installed in your shell.
-
-### (Optional) Install kubectl
-
-`kubectl` is the command-line tool for Kubernetes. `kubectl` is useful if you 
-want to interact with your Kubeflow cluster locally. If you decide not to use
-`kubectl`, you can skip the steps later in the tutorial that use the command.
-
-Run the following Cloud SDK command to install `kubectl`:
-
-```
-gcloud components install kubectl
-```
+* This guide assumes you want to manage your GCP environment in the [Cloud Shell][cloud-shell] environment rather than your own server. If you choose to use a local server instead, some of the components (such as Cloud SDK) must be manually installed.
 
 ### Set up some handy environment variables
 
@@ -131,7 +122,7 @@ Set up the following environment variables for use throughout the tutorial:
     ```
     export DEPLOYMENT_NAME=kubeflow
     ```
-
+    
 ### Deploy Kubeflow
 
 Deploy Kubeflow on GCP:
@@ -146,7 +137,7 @@ Deploy Kubeflow on GCP:
     credentials**, use the same value for the `<deployment_name>` as you used
     when setting up the `DEPLOYMENT_NAME` environment variable earlier in this
     tutorial.
-  * Choose **Kubeflow version v0.4.1** or later.
+  * Choose **Kubeflow version {{% kubeflow-current-version %}}** or later.
 
     The following screenshot shows the Kubeflow deployment UI with hints about
     the value for each input field:
@@ -158,7 +149,7 @@ Deploy Kubeflow on GCP:
 1. (Optional) If you want to examine your cluster while waiting for the UI to
    be available, you can use `kubectl` to connect to your cluster:
 
-  * Connect your local `kubectl` session to the cluster:
+  * Connect your cloud shell session to the cluster:
 
       ```
       gcloud container clusters get-credentials \
@@ -264,7 +255,7 @@ If you don't have a Python 3 environment set up, install
   environment, run the following commands:   
 
     ```bash
-    apt-get update; apt-get install -y wget bzip2
+    apt-get update; 
     wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
     bash Miniconda3-latest-Linux-x86_64.sh
     ```
@@ -400,6 +391,10 @@ UI.
     classification label from 0 to 9. Each bar represents
     the probability that the image matches the respective label.
 
+Notes:
+
+* You can find your trained model data in the bucket path you entered in step 5 of this procedure.
+
 ## Understanding the pipeline definition code
 
 The pipeline is defined in the Python file `mnist_pipeline.py` which you
@@ -610,4 +605,5 @@ SDK](/docs/pipelines/sdk/sdk-overview/).
 [gsutil-mb]: https://cloud.google.com/storage/docs/gsutil/commands/mb
 [gsutil-acl-ch]: https://cloud.google.com/storage/docs/gsutil/commands/acl#ch
 
-
+[granting-changing-revoking-access]: https://cloud.google.com/iam/docs/granting-changing-revoking-access#quick_updates_using_the_gcloud_command-line_tool
+[release-page]:https://github.com/kubeflow/kubeflow/releases

layouts/shortcodes/kubeflow-current-version.html

@@ -0,0 +1 @@
+0.5.1