Update v2pipelines and UI (#28)

* update-v2pipelines-and-ui

* updates after rc1 test

* minor update

* peer review edits

workshop/docs/modules/ROOT/nav.adoc

@@ -15,8 +15,8 @@
 * 3. Model Serving
 ** xref:preparing-a-model-for-deployment.adoc[1. Saving the Model]
 ** xref:deploying-a-model.adoc[2. Deploy the Model]
-*** xref:deploying-a-model-multi-model-server.adoc[1. Deploying on a multi-model server]
-*** xref:deploying-a-model-single-model-server.adoc[2. Deploying on a single-model server]
+*** xref:deploying-a-model-single-model-server.adoc[1. Deploying on a single-model server]
+*** xref:deploying-a-model-multi-model-server.adoc[2. Deploying on a multi-model server]
 ** xref:testing-the-model-api.adoc[3. Using the API]
 
 * 4. Data Science Pipelines

workshop/docs/modules/ROOT/pages/_attributes.adoc

@@ -3,4 +3,5 @@
 :deliverable: workshop
 //:deliverable: tutorial
 :productname-long: Red Hat OpenShift AI
-:productname-short: OpenShift AI
+:productname-short: OpenShift AI
+:version: 2.9

workshop/docs/modules/ROOT/pages/automating-workflows-with-pipelines.adoc

@@ -23,7 +23,7 @@ You've created a blank pipeline!
 
 . Set the default runtime image for when you run your notebook or Python code.
 
-.. In the pipeline editor, click *Open Panel* 
+.. In the pipeline editor, click *Open Panel*.
 +
 image::pipelines/wb-pipeline-panel-button-loc.png[Open Panel]
 
@@ -88,6 +88,8 @@ In node 1, the notebook creates the `models/fraud/1/model.onnx` file. In node 2,
 image::pipelines/wb-pipeline-node-1-file-output-form.png[Set file dependency value, 400]
 
 . Repeat steps 1-3 for node 2.
+
+. Save the pipeline.
 
 == Configure the data connection to the S3 storage bucket
 
@@ -122,7 +124,7 @@ You must set the secret name and key for each of these fields.
 
 .. Select node 2, and then select the *Node Properties* tab.
 +
-Under *Additional Properties*, note that some environment variables have been pre-filled. The pipeline editor inferred that you'd need them from the notebook code.  
+Under *Additional Properties*, note that some environment variables have been pre-filled. The pipeline editor inferred that you need them from the notebook code.  
 +
 Since you don't want to save the value in your pipelines, remove all of these environment variables.
 
@@ -143,7 +145,7 @@ image::pipelines/wb-pipeline-add-kube-secret.png[Add Kube Secret]
 +
 image::pipelines/wb-pipeline-kube-secret-form.png[Secret Form, 400]
 
-.. Repeat Steps 3a and 3b for each set of these Kubernetes secrets:
+.. Repeat Steps 2a and 2b for each set of these Kubernetes secrets:
 
 * *Environment Variable*: `AWS_SECRET_ACCESS_KEY`
 ** *Secret Name*: `aws-connection-my-storage`
@@ -171,7 +173,8 @@ Upload the pipeline on your cluster and run it. You can do so directly from the
 
 . Click the play button in the toolbar of the pipeline editor.
 +
-image::pipelines/wb-pipeline-run-button.png[Pipeline Run Button]
+image::pipelines/wb-pipeline-run-button.png[Pipeline Run Button, 300]
+
 
 . Enter a name for your pipeline.
 . Verify the *Runtime Configuration:* is set to `Data Science Pipeline`.  

workshop/docs/modules/ROOT/pages/creating-a-workbench.adoc

@@ -49,7 +49,7 @@ image::workbenches/ds-project-workbench-list.png[Workbench list]
 
 NOTE: If you made a mistake, you can edit the workbench to make changes.
 
-image::workbenches/ds-project-workbench-list-edit.png[Workbench list edit]
+image::workbenches/ds-project-workbench-list-edit.png[Workbench list edit, 300]
 
 
 .Next step

workshop/docs/modules/ROOT/pages/enabling-data-science-pipelines.adoc

@@ -38,15 +38,21 @@ You must wait until the pipeline configuration is complete before you continue a
 
 .Verification
 
-Check the *Pipelines* tab for the project. Pipelines are enabled when the *Configure pipeline server* button no longer appears.
+. Navigate to the *Pipelines* tab for the project.
+. Next to *Import pipeline*, click the action menu (⋮) and then select *View pipeline server configuration*.
++
+image::projects/ds-project-pipeline-server-view.png[View pipeline server configuration menu, 300]
++
+An information box opens and displays the object storage connection information for the pipeline server.
 
-image::projects/ds-project-create-pipeline-server-complete.png[Create pipeline server complete]
 
 [NOTE]
 ====
-If you have waited more than 5 minutes and the pipeline server configuration does not complete, you can try to delete the pipeline server and create it again.
+If you have waited more than 5 minutes, and the pipeline server configuration does not complete, you can try to delete the pipeline server and create it again.
+
+image::projects//ds-project-delete-pipeline-server.png[Delete pipeline server, 300]
 
-image::projects//ds-project-delete-pipeline-server.png[Delete pipeline server]
+You can also ask your {productname-short} administrator to verify that self-signed certificates are added to your cluster as described in https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/{version}/html/installing_and_uninstalling_openshift_ai_self-managed/working-with-certificates_certs[Working with certificates].
 ====
 
 .Next step

workshop/docs/modules/ROOT/pages/importing-files-into-jupyter.adoc

@@ -21,7 +21,7 @@ This file-browser window shows the files and folders that are saved inside your
 
 .. On the toolbar, click the *Git Clone* icon:
 +
-image::workbenches/jupyter-git-icon.png[Git Clone icon, 200]
+image::workbenches/jupyter-git-icon.png[Git Clone icon, 300]
 
 .. Enter the following {deliverable} Git *https* URL:
 +
@@ -32,21 +32,21 @@ image::workbenches/jupyter-git-icon.png[Git Clone icon, 200]
 https://github.com/rh-aiservices-bu/fraud-detection.git
 ----
 +
-image::workbenches/jupyter-git-modal.png[Git Modal]
+image::workbenches/jupyter-git-modal.png[Git Modal, 300]
 
 .. Check the *Include submodules* option.
 
-.. Click *CLONE*.
+.. Click *Clone*.
 
 .Verification
 
 Double-click the newly-created folder, `fraud-detection`:
 
-image::workbenches/jupyter-file-browser.png[Jupyter file browser]
+image::workbenches/jupyter-file-browser.png[Jupyter file browser, 300]
 
 In the file browser, you should see the notebooks that you cloned from Git.
 
-image::workbenches/jupyter-file-browser-2.png[Jupyter file browser - fraud-detection]
+image::workbenches/jupyter-file-browser-2.png[Jupyter file browser - fraud-detection, 300]
 
 
 .Next step

workshop/docs/modules/ROOT/pages/index.adoc

@@ -30,8 +30,11 @@ Based on this data, the model outputs the likelihood of the transaction being fr
 
 == Before you begin
 
-https://developers.redhat.com/products/red-hat-openshift-ai/download[Set up your {productname-long} environment]
+If you don't already have an instance of {productname-long}, see the https://developers.redhat.com/products/red-hat-openshift-ai/download[{productname-long} page on the Red Hat Developer website] for information on setting up your environment. There, you can create an account and access the *free {productname-short} Sandbox* or you can learn how to install {productname-short} on *your own OpenShift cluster*.
 
-If you don't already have an instance of {productname-long}, find out more on the https://developers.redhat.com/products/red-hat-openshift-ai/download[developer page]. There, you can create an account and access the *free {productname-short} Sandbox* or you can learn how to install {productname-short} on *your own OpenShift cluster*.
+[IMPORTANT]
+====
+If your cluster uses self-signed certificates, before you begin the {deliverable}, your {productname-short} administrator must add self-signed certificates for {productname-short} as described in https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/{version}/html/installing_and_uninstalling_openshift_ai_self-managed/working-with-certificates_certs[Working with certificates].
+====
 
 If you're ready, xref:navigating-to-the-dashboard.adoc[start the {deliverable}!]

workshop/docs/modules/ROOT/pages/running-a-pipeline-generated-from-python-code.adoc

@@ -1,7 +1,7 @@
 [id='running-a-pipeline-generated-from-python-code']
 = Running a data science pipeline generated from Python code
 
-In the previous section, you created a simple pipeline by using the GUI pipeline editor. It's often desirable to create pipelines by using code that can be version-controlled and shared with others. The https://github.com/kubeflow/kfp-tekton[kfp-tekton] SDK provides a Python API for creating pipelines. The SDK is available as a Python package that you can install by using the `pip install kfp-tekton~=1.5.9` command. With this package, you can use Python code to create a pipeline and then compile it to Tekton YAML. Then you can import the YAML code into {productname-short}.
+In the previous section, you created a simple pipeline by using the GUI pipeline editor. It's often desirable to create pipelines by using code that can be version-controlled and shared with others. The https://github.com/kubeflow/pipelines[kfp] SDK provides a Python API for creating pipelines. The SDK is available as a Python package that you can install by using the `pip install kfp` command. With this package, you can use Python code to create a pipeline and then compile it to YAML format. Then you can import the YAML code into {productname-short}.
 
 This {deliverable} does not delve into the details of how to use the SDK. Instead, it provides the files for you to view and upload.
 
@@ -31,7 +31,7 @@ image::pipelines/dsp-pipline-import-upload.png[]
 +
 The pipeline shows in the list of pipelines.
 
-. Expand the pipeline item and then click *View runs*.
+. Expand the pipeline item, click the action menu (⋮), and then select *View runs*.
 +
 image::pipelines/dsp-pipline-view-runs.png[]
 
@@ -47,8 +47,6 @@ image::pipelines/pipeline-create-run-form.png[Create Pipeline Run form]
 
 . Click *Create* to create the run.
 +
-A new run starts immediately and opens the run details page.
+A new run starts immediately. The *Details* page shows a pipeline created in Python that is running in {productname-short}.
 +
-image::pipelines/pipeline-run-in-progress.png[]
-
-There you have it:  a pipeline created in Python that is running in {productname-short}.
+image::pipelines/pipeline-run-in-progress.png[]

workshop/docs/modules/ROOT/pages/running-a-script-to-install-storage.adoc

@@ -21,7 +21,7 @@ You must know the OpenShift resource name for your data science project so that
 
 In the {productname-short} dashboard, select *Data Science Projects* and then click the *?* icon next to the project name. A text box appears with information about the project, including its resource name:
 
-image::projects/ds-project-list-resource-hover.png[Project list resource name]
+image::projects/ds-project-list-resource-hover.png[Project list resource name, 400]
 
 
 [NOTE]

workshop/docs/modules/ROOT/pages/running-code-in-a-notebook.adoc

@@ -21,7 +21,7 @@ You can run a code cell from the notebook interface or from the keyboard:
 +
 image::workbenches/run_button.png[Jupyter Run]
 
-* *From the keyboard:* Press `CTRL`+`ENTER` to run a cell or press `SHIFT`+`ENTER` to run the cell and automatically select the next one.
+* *From the keyboard:* Press `CTRL` + `ENTER` to run a cell or press `SHIFT` + `ENTER` to run the cell and automatically select the next one.
 
 After you run a cell, you can see the result of its code as well as information about when the cell was run, as shown in this example:
 

workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc

@@ -13,11 +13,11 @@ Note that you can start a Jupyter notebook from here, but it would be a one-off
 +
 image::projects/dashboard-click-projects.png[Data Science Projects List]
 +
-If you already have an active project that you'd like to use, select it now and skip ahead to the next section, xref:storing-data-with-data-connections.adoc[Storing data with data connections]. Otherwise, continue to the next step.
+If you already have an active project that want to use, select it now and skip ahead to the next section, xref:storing-data-with-data-connections.adoc[Storing data with data connections]. Otherwise, continue to the next step.
 
 . Click *Create data science project*.
 
-. Enter a display name and description. Based on the display name, a resource name is automatically generated, but you can change it if you'd like.
+. Enter a display name and description. Based on the display name, a resource name is automatically generated, but you can change if you prefer.
 +
 image::projects/ds-project-new-form.png[New data science project form]