doc-fixes-2.5 (#12)

* doc-fixes-2.5

* doc-fixes-2.5 update image

* doc update set attributes to rhoai

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

@@ -2,7 +2,5 @@
 :source-highlighter: highlightjs
 :deliverable: workshop
 //:deliverable: tutorial
-:productname-long: Red Hat OpenShift Data Science
-:productname-short: OpenShift Data Science
-//:productname-long: Red Hat OpenShift AI
-//:productname-short: OpenShift AI
+:productname-long: Red Hat OpenShift AI
+:productname-short: OpenShift AI

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

@@ -25,7 +25,7 @@ Red Hat provides several supported notebook images. In the *Notebook image* sect
 +
 image::workbenches/create-workbench-form-image.png[Workbench image, 600]
 
-. Select a small deployment and no GPUs. This {deliverable} does not require any GPUs.
+. Choose a small deployment.
 +
 image::workbenches/create-workbench-form-size.png[Workbench size,600]
 

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` 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/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}.
 
 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.
 

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

@@ -45,6 +45,8 @@ image::projects/ocp-console-project-selected.png[Selected project]
 
 . Copy the following code and paste it into the *Import YAML* editor.
 +
+*Note:* This code gets and applies the `setup-s3-no-sa.yaml` file.
++
 [.lines_space]
 [.console-input]
 [source, yaml]