Updated screenshots and UI info for Kubeflow v0.6

content/docs/components/training/tftraining.md

@@ -211,7 +211,7 @@ kubectl apply -f tfevent-volume
 kubectl apply -f tf_job_mnist.yaml
 ```
 
-Monitor the job (see the [TFJob docs](/docs/components/tftraining/#monitoring-your-job)):
+Monitor the job (see the [detailed guide below](#monitoring-your-job)):
 
 ```
 kubectl -n kubeflow get tfjob mnist -o yaml
@@ -238,12 +238,8 @@ Typically you can change the following values in the TFJob yaml file:
 
 ### Accessing the TFJob dashboard
 
-The TFJob dashboard is available at `<path>/tfjobs/ui/`. Specifically:
-
-* If you're using the central Kubeflow UI, you can access the TFJob dashboard
-  by clicking **TFJOB DASHBOARD**:
-
-    ![Central UI](/docs/images/central-ui.png)
+The TFJob dashboard has the title **kubeflow/tf-operator**. 
+You can access it at `<path>/tfjobs/ui/`. Specifically:
 
 * If you followed the
  guide to [deploying Kubeflow on GCP](/docs/gke/deploy/), you can

content/docs/gke/pipelines-tutorial.md

@@ -87,6 +87,12 @@ Notes:
   [clean up your GCP resources](#cleanup) when you've finished with them.
 * This guide uses [Cloud Shell][cloud-shell] to manage your GCP environment, to save you the steps of installing [Cloud SDK][cloud-sdk] and [kubectl][kubectl]. 
 
+### Start your Cloud Shell
+
+Follow the link to activate a 
+[Cloud Shell environment](https://console.cloud.google.com/cloudshell) in your 
+browser.
+
 ### Set up some handy environment variables
 
 Set up the following environment variables for use throughout the tutorial:
@@ -190,7 +196,7 @@ Deploy Kubeflow on GCP:
         alt="Prediction UI"
         class="mt-3 mb-3 p-3 border border-info rounded">
 
-1. Click **Pipeline Dashboard** to access the pipelines UI. The pipelines UI
+1. Click **Pipelines** to access the pipelines UI. The pipelines UI
    looks like this:
     <img src="/docs/images/pipelines-ui.png" 
       alt="Pipelines UI"
@@ -606,7 +612,7 @@ SDK](/docs/pipelines/sdk/sdk-overview/).
 [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
+[cloud-shell]: https://cloud.google.com/shell/
 [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

content/docs/notebooks/setup.md

@@ -19,10 +19,11 @@ in Kubeflow.
 
 Summary of steps:
 
-1. Follow the [Kubeflow getting-started guide](/docs/started/getting-started/) to
-set up your Kubeflow deployment and open the Kubeflow UI.
+1. Follow the [Kubeflow getting-started guide](/docs/started/getting-started/) 
+  to set up your Kubeflow deployment and open the Kubeflow UI.
 
-1. Click **Notebooks** in the left-hand panel of the Kubeflow UI.
+1. Click **Notebook Servers** in the left-hand panel of the Kubeflow UI.
+1. Choose the **namespace** corresponding to your Kubeflow profile.
 1. Click **NEW SERVER** to create a notebook server.
 1. When the notebook server provisioning is complete, click **CONNECT**.
 1. Click **Upload** to upload an existing notebook, or click **New** to
@@ -58,8 +59,8 @@ getting-started guide for your chosen environment. For example:
 
 ## Create a Jupyter notebook server and add a notebook
 
-1. Click **Notebooks** in the left-hand panel of the Kubeflow UI to access the
-  Jupyter notebook services deployed with Kubeflow:
+1. Click **Notebook Servers** in the left-hand panel of the Kubeflow UI to 
+  access the Jupyter notebook services deployed with Kubeflow:
   <img src="/docs/images/jupyterlink.png" 
     alt="Opening notebooks from the Kubeflow UI"
     class="mt-3 mb-3 border border-info rounded">
@@ -69,22 +70,34 @@ getting-started guide for your chosen environment. For example:
      to your Google Account you may not need to log in again.)
    * On all other platforms, sign in using any username and password.
 
+1. Select a namespace:
+   * Click the namespace dropdown to see the list of available namespaces.
+   * Choose the namespace that corresponds to your Kubeflow profile. (See
+     the page on [multi-user isolation](/docs/other-guides/multi-user-overview/) 
+     for more information about namespaces.)
+
+    <img src="/docs/images/notebooks-namespace.png" 
+      alt="Selecting a Kubeflow namespace"
+      class="mt-3 mb-3 border border-info rounded">
+
 1. Click **NEW SERVER** on the **Notebook Servers** page:
 
     <img src="/docs/images/add-notebook-server.png" 
       alt="The Kubeflow notebook servers page"
       class="mt-3 mb-3 border border-info rounded">
 
-    You should see the **New Notebook Server** page:
+    You should see a page for entering details of your new server. Here is a 
+    partial screenshot of the page:
 
     <img src="/docs/images/new-notebook-server.png" 
       alt="Form for adding a Kubeflow notebook server"
       class="mt-3 mb-3 border border-info rounded">
 
 1. Enter a **name** of your choice for the notebook server. The name can
   include letters and numbers, but no spaces. For example, `my-first-notebook`.
-1. Enter a **namespace** to identify the project group or team to which this 
-  notebook server belongs. The default is `kubeflow`.
+1. Kubeflow automatically updates the value in the **namespace** field to
+  be the same as the namespace that you selected in a previous step. This 
+  ensures that the new notebook server is in a namespace that you can access.
 
 1. Select a Docker **image** for the baseline deployment of your notebook 
   server. You can choose from a range of *standard* images or specify a 
@@ -150,7 +163,7 @@ getting-started guide for your chosen environment. For example:
   volumes or specify existing volumes. Kubeflow provisions a 
   [Kubernetes persistent volume (PV)](https://kubernetes.io/docs/concepts/storage/persistent-volumes/) for each of your data volumes.
 
-1. Click **SPAWN** and wait a while. You should see an entry for your new
+1. Click **LAUNCH**. You should see an entry for your new
   notebook server on the **Notebook Servers** page, with a spinning indicator in 
   the **Status** column. It can take a few minutes to set up
   the notebook server.

content/docs/other-guides/accessing-uis.md

@@ -9,50 +9,45 @@ instructions on how to connect to them.
 
 ## Accessing Kubeflow web UIs
 
-Kubeflow comes with a number of web UIs, including:
+The Kubeflow web UIs include the following:
 
-* Central UI for navigation
-* Jupyter notebooks
-* TFJob Dashboard
-* Katib Dashboard
-* Pipelines Dashboard
-* Artifact Store Dashboard
+* A central **Kubeflow** UI for navigation between the Kubeflow applications.
+* **Pipelines** for a Kubeflow Pipelines dashboard
+* **Notebook Servers** for Jupyter notebooks.
+* **Katib** for hyperparameter tuning.
+* **Artifact Store** for tracking of artifact metadata.
+* **tf-operator** for a TFJob dashboard.
 
-To make it easy to connect to these UIs Kubeflow provides a left hand navigation
-bar for navigating between the different applications.
+Instructions below indicate how to connect to the Kubeflow central UI. From
+there you can navigate to the different services using the left hand navigation
+bar. 
 
-Instructions below indicate how to connect to the Kubeflow landing page. From
-there you can easily navigate to the different services using the left hand navigation
-bar. The landing page looks like this:
+The central UI dashboard looks like this:
 
 <img src="/docs/images/central-ui.png"
-  alt="Kubeflow UI"
+  alt="Kubeflow central UI"
   class="mt-3 mb-3 border border-info rounded">
 
+## URL pattern with Google Cloud Platform (GCP)
 
-## Google Cloud Platform (Kubernetes Engine)
-
-If you followed the guide to [deploying Kubeflow on Google Cloud Platform
-(GCP)](/docs/gke/deploy/), Kubeflow
-is deployed with Cloud Identity-Aware Proxy (Cloud IAP) or basic authentication,
-and the Kubeflow landing page is accessible at a URL of the following pattern:
+If you followed the guide to [deploying Kubeflow on GCP](/docs/gke/deploy/), 
+the Kubeflow central UI is accessible at a URL of the following pattern:
 
 ```
-https://<name>.endpoints.<project>.cloud.goog/
+https://<application-name>.endpoints.<project-ID>.cloud.goog/
 ```
 
-This URL brings up the landing page illustrated above.
+The URL brings up the dashboard illustrated above.
 
-When deployed with Cloud IAP, Kubeflow uses the
+If you deploy Kubeflow with Cloud Identity-Aware Proxy (IAP), Kubeflow uses the
 [Let's Encrypt](https://letsencrypt.org/) service to provide an SSL certificate
 for the Kubeflow UI. For troubleshooting issues with your certificate, see the
 guide to
 [monitoring your Cloud IAP setup](/docs/gke/deploy/monitor-iap-setup/).
 
-## Using Kubectl and port-forwarding
+## Using kubectl and port-forwarding
 
-If you're not using the Cloud IAP option or if you haven't yet set up your
-Kubeflow endpoint, you can access Kubeflow via `kubectl` and port-forwarding.
+You can access Kubeflow via `kubectl` and port-forwarding as follows:
 
 1. Install `kubectl` if you haven't already done so:
 
@@ -72,18 +67,21 @@ Kubeflow endpoint, you can access Kubeflow via `kubectl` and port-forwarding.
     http://localhost:8080/
     ```
 
-  * This will only work if you haven't enabled basic auth or Cloud IAP. If
-    authentication is enabled requests will be rejected
-    because you are not connecting over HTTPS and attaching proper credentials.
+  * Port-forwarding will not work if you're using basic authentication with GCP. 
 
-  * Depending on how you've configured Kubeflow, not all UIs will work behind port-forwarding to the reverse proxy.
+  * Depending on how you've configured Kubeflow, not all UIs work behind 
+    port-forwarding to the reverse proxy.
 
-    * Some web applications need to be configured to know the base URL they are serving on.
-    * So if you deployed Kubeflow with an ingress serving at `https://acme.mydomain.com` and configured an application
-      to be served at the URL `https://acme.mydomain.com/myapp` then the app may not work when served on
-      `https://localhost:8080/myapp` because the paths do not match.
+        For some web applications, you need to configure the base URL on which
+        the app is serving.
+
+        For example, if you deployed Kubeflow with an ingress serving at 
+        `https://example.mydomain.com` and configured an application
+        to be served at the URL `https://example.mydomain.com/myapp`, then the 
+        app may not work when served on
+        `https://localhost:8080/myapp` because the paths do not match.
 
 ## Next steps
 
-See how to [set up your Jupyter notebooks](/docs/notebooks/setup/) in
-Kubeflow.
+* See how to [access the TFJob dashboard](/docs/components/training/tftraining/).
+* [Set up your Jupyter notebooks](/docs/notebooks/setup/) in Kubeflow.

content/docs/pipelines/pipelines-quickstart.md

@@ -19,9 +19,7 @@ Kubeflow Pipelines. If you need a more in-depth guide, see the
 
 Follow these steps to deploy Kubeflow and open the pipelines dashboard:
 
-1. Follow the guide to [deploying Kubeflow on GCP](/docs/gke/deploy/), 
-  including the step to deploy Kubeflow using the 
-  [Kubeflow deployment UI](https://deploy.kubeflow.cloud/).
+1. Follow the guide to [deploying Kubeflow on GCP](/docs/gke/deploy/).
 
     {{% pipelines-compatibility %}} 
 
@@ -44,7 +42,7 @@ Follow these steps to deploy Kubeflow and open the pipelines dashboard:
 
     1. Run ```kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80``` and go to `http://localhost:8080/`.
 
-1. Click **Pipeline Dashboard** to access the pipelines UI. The pipelines UI looks like
+1. Click **Pipelines** to access the pipelines UI. The pipelines UI looks like
   this:
   <img src="/docs/images/pipelines-ui.png" 
     alt="Pipelines UI"
@@ -57,13 +55,13 @@ pipelines quickly. The steps below show you how to run a basic sample that
 includes some Python operations, but doesn't include a machine learning (ML) 
 workload:
 
-1. Click the name of the sample, **\[Sample\] Basic - Parallel Join**, on the pipelines 
+1. Click the name of the sample, **\[Sample\] Basic - Parallel Execution**, on the pipelines 
   UI:
   <img src="/docs/images/click-pipeline-sample.png" 
     alt="Pipelines UI"
     class="mt-3 mb-3 border border-info rounded">
 
-1. Click **Create an experiment**:
+1. Click **Create experiment**:
   <img src="/docs/images/pipelines-start-experiment.png" 
     alt="Starting an experiment on the pipelines UI"
     class="mt-3 mb-3 border border-info rounded">
@@ -90,7 +88,7 @@ workload:
 
 You can find the source code for the basic parallel join sample in the 
 [Kubeflow Pipelines 
-repo](https://github.com/kubeflow/pipelines/blob/master/samples/basic/parallel_join.py).
+repo](https://github.com/kubeflow/pipelines/blob/master/samples/core/parallel_join/parallel_join.py).
 
 ## Run an ML pipeline
 
@@ -124,7 +122,7 @@ Follow these steps to set up the necessary GCP services and run the sample:
     alt="XGBoost sample on the pipelines UI"
     class="mt-3 mb-3 border border-info rounded">
 
-1. Click **Create an experiment**.
+1. Click **Create experiment**.
 1. Follow the prompts to create an **experiment** and then create a **run**.
   Supply the following **run parameters**:
 
@@ -163,7 +161,7 @@ Follow these steps to set up the necessary GCP services and run the sample:
 
 You can find the source code for the XGBoost training sample in the 
 [Kubeflow Pipelines 
-repo](https://github.com/kubeflow/pipelines/tree/master/samples/xgboost-spark).
+repo](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost-spark).
 
 ## Clean up your GCP environment
 

layouts/shortcodes/code-webui-port-forward.html

@@ -1,3 +1,3 @@
-<pre><code>export NAMESPACE=kubeflow
+<pre><code>export NAMESPACE=istio-system
 kubectl port-forward svc/ambassador -n ${NAMESPACE} 8080:80
 </code></pre>