docs-add-feedback (#64)

MelissaFlinn · web-flow · commit 26bb52370eb9 · 2025-02-06T16:39:31.000-05:00
* docs-add-feedback

* docs-add-feedback updated navbar

* more doc updates

* docs--peer-review
diff --git a/workshop/docs/modules/ROOT/nav.adoc b/workshop/docs/modules/ROOT/nav.adoc
@@ -2,8 +2,8 @@
 ** xref:navigating-to-the-dashboard.adoc[1. The Dashboard]
 ** xref:setting-up-your-data-science-project.adoc[2. Data Science Projects]
 ** xref:storing-data-with-connections.adoc[3. Storage Data Connections]
-*** xref:creating-connections-to-storage.adoc[1. Manual]
-*** xref:running-a-script-to-install-storage.adoc[2. Scripted Local]
+*** xref:running-a-script-to-install-storage.adoc[1. Scripted Local]
+*** xref:creating-connections-to-storage.adoc[2. Manual]
 ** xref:enabling-data-science-pipelines.adoc[4. Enable Pipelines]
 
 * 2. Workbenches
diff --git a/workshop/docs/modules/ROOT/pages/automating-workflows-with-pipelines.adoc b/workshop/docs/modules/ROOT/pages/automating-workflows-with-pipelines.adoc
@@ -9,6 +9,11 @@ Your completed pipeline should look like the one in the `6 Train Save.pipeline`
 
 To explore the pipeline editor, complete the steps in the following procedure to create your own pipeline. Alternately, you can skip the following procedure and instead run the `6 Train Save.pipeline` file.
 
+== Prerequisites
+
+* You configured a pipeline server as described in xref:enabling-data-science-pipelines.adoc[Enabling data science pipelines].
+* If you configured the pipeline server after you created your workbench, you stopped and then started your workbench.
+
 == Create a pipeline
 
 . Open your workbench's JupyterLab environment. If the launcher is not visible, click *+* to open it.
@@ -178,14 +183,31 @@ Upload the pipeline on your cluster and run it. You can do so directly from the
 +
 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`.  
+. Verify that the *Runtime Configuration:* is set to `Data Science Pipeline`.  
 . Click *OK*.
 +
-NOTE: If `Data Science Pipeline` is not available as a runtime configuration, you may have created your notebook before the pipeline server was available.  You can restart your notebook after the pipeline server has been created in your data science project.
+[NOTE]
+====
+If you see an error message stating that "no runtime configuration for Data Science Pipeline is defined", you might have created your workbench before the pipeline server was available. 
+
+To address this situation, you must verify that you configured the pipeline server and then restart the workbench.
+
+Follow these steps in the {productname-short} dashboard:
+
+. Check the status of the pipeline server:
+.. In your Fraud Detection project, click the *Pipelines* tab.
+** If you see the *Configure pipeline server* option, follow the steps in xref:enabling-data-science-pipelines[Enabling data science pipelines]. 
+** If you see the *Import a pipeline* option, the pipeline server is configured. Continue to the next step.
+. Restart your Fraud Detection workbench:
+.. Click the *Workbenches* tab.
+.. Click *Stop* and then click *Stop workbench*. 
+.. After the workbench status is *Stopped*, click *Start*.
+.. Wait until the workbench status is *Running*.
+. Return to your workbench's JupyterLab environment and run the pipeline. 
+====
 
-. Return to your data science project and expand the newly created pipeline.
+. In the {productname-short} dashboard, open your data science project and expand the newly created pipeline.
 +
 image::pipelines/dsp-pipeline-complete.png[New pipeline expanded, 800]
 
@@ -202,4 +224,4 @@ The result should be a `models/fraud/1/model.onnx` file in your S3 bucket which
 
 .Next step
 
-(optional) xref:running-a-pipeline-generated-from-python-code.adoc[Running a data science pipeline generated from Python code]
+(Optional) xref:running-a-pipeline-generated-from-python-code.adoc[Running a data science pipeline generated from Python code]
diff --git a/workshop/docs/modules/ROOT/pages/creating-a-workbench.adoc b/workshop/docs/modules/ROOT/pages/creating-a-workbench.adoc
@@ -1,13 +1,13 @@
 [id='creating-a-workbench']
-= Creating a workbench and selecting a notebook image
+= Creating a workbench and selecting a workbench image
 
-A workbench is an instance of your development and experimentation environment. Within a workbench you can select a notebook image for your data science work.
+A workbench is an instance of your development and experimentation environment. When you create a workbench, you select a workbench image (sometimes referred to as a notebook image) that is optimized with the tools and libraries that you need for developing models. 
 
 .Prerequisites
 
 * You created a `My Storage` connection as described in xref:storing-data-with-connections.adoc[Storing data with connections].
 
-* You configured a pipeline server as described in xref:enabling-data-science-pipelines.adoc[Enabling data science pipelines].
+* If you intend to complete the pipelines section of this {deliverable}, you configured a pipeline server as described in xref:enabling-data-science-pipelines.adoc[Enabling data science pipelines].
 
 
 .Procedure
@@ -22,7 +22,7 @@ image::workbenches/ds-project-create-workbench.png[Create workbench button, 800]
 +
 image::workbenches/create-workbench-form-name-desc.png[Workbench name and description, 600]
 +
-{org-name} provides several supported notebook images. In the *Notebook image* section, you can choose one of these images or any custom images that an administrator has set up for you. The *Tensorflow* image has the libraries needed for this {deliverable}.
+{org-name} provides several supported workbench images. In the *Notebook image* section, you can choose one of the default images or a custom image that an administrator has set up for you. The *Tensorflow* image has the libraries needed for this {deliverable}.
 
 . Select the latest *Tensorflow* image.
 +
@@ -57,4 +57,4 @@ image::workbenches/ds-project-workbench-list-edit.png[Workbench list edit, 350]
 
 .Next step
 
-xref:importing-files-into-jupyter.adoc[Importing the {deliverable} files into the Jupyter environment]
+xref:importing-files-into-jupyter.adoc[Importing the {deliverable} files into the JupyterLab environment]
diff --git a/workshop/docs/modules/ROOT/pages/creating-connections-to-storage.adoc b/workshop/docs/modules/ROOT/pages/creating-connections-to-storage.adoc
@@ -55,7 +55,7 @@ In the *Connections* tab for the project, check to see that your connections are
 image::projects/ds-project-connections.png[List of project connections, 500]
 
 
-.Next steps
+.Next step
 
 If you want to complete the pipelines section of this {deliverable}, go to xref:enabling-data-science-pipelines.adoc[Enabling data science pipelines].
 
diff --git a/workshop/docs/modules/ROOT/pages/enabling-data-science-pipelines.adoc b/workshop/docs/modules/ROOT/pages/enabling-data-science-pipelines.adoc
@@ -1,13 +1,13 @@
 [id='enabling-data-science-pipelines']
 = Enabling data science pipelines
 
-NOTE: If you do not intend to complete the pipelines section of the workshop you can skip this step and move on to the next section, xref:creating-a-workbench.adoc[Create a Workbench].
+NOTE: If you do not intend to complete the pipelines section of this {deliverable} you can skip this step and move on to the next section, xref:creating-a-workbench.adoc[Create a Workbench].
 
 In this section, you prepare your {deliverable} environment so that you can use data science pipelines.
 
-In this {deliverable}, you implement an example pipeline by using the JupyterLab Elyra extension. With Elyra, you can create a visual end-to-end pipeline workflow that can be executed in OpenShift AI.
+Later in this {deliverable}, you implement an example pipeline by using the JupyterLab Elyra extension. With Elyra, you can create a visual end-to-end pipeline workflow that can be executed in {productname-short}.
 
-.Prerequisite
+.Prerequisites
 
 * You have installed local object storage buckets and created connections, as described in xref:storing-data-with-connections.adoc[Storing data with connections].
 
@@ -34,7 +34,7 @@ image::projects/ds-project-create-pipeline-server-form.png[Selecting the Pipelin
 You must wait until the pipeline configuration is complete before you continue and create your workbench. If you create your workbench before the pipeline server is ready, your workbench will not be able to submit pipelines to it.
 ====
 +
-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 delete the pipeline server and create it again.
 +
 image::projects//ds-project-delete-pipeline-server.png[Delete pipeline server, 250]
 +
diff --git a/workshop/docs/modules/ROOT/pages/importing-files-into-jupyter.adoc b/workshop/docs/modules/ROOT/pages/importing-files-into-jupyter.adoc
@@ -1,9 +1,9 @@
 [id='importing-files-into-jupyter']
-= Importing the {deliverable} files into the Jupyter environment
+= Importing the {deliverable} files into the JupyterLab environment
 
 :git-version: main
 
-The Jupyter environment is a web-based environment, but everything you do inside it happens on *{productname-long}* and is powered by the *OpenShift* cluster. This means that, without having to install and maintain anything on your own computer, and without disposing of valuable local resources such as CPU, GPU and RAM, you can conduct your Data Science work in this powerful and stable managed environment.
+The JupyterLab environment is a web-based environment, but everything you do inside it happens on *{productname-long}* and is powered by the *OpenShift* cluster. This means that, without having to install and maintain anything on your own computer, and without using valuable local resources such as CPU, GPU and RAM, you can conduct your data science work in this powerful and stable managed environment.
 
 .Prerequisites
 
@@ -15,11 +15,11 @@ You created a workbench, as described in xref:creating-a-workbench.adoc[Creating
 +
 image::workbenches/ds-project-workbench-open.png[Open workbench]
 +
-Your Jupyter environment window opens.
+Your JupyterLab environment window opens.
 +
 This file-browser window shows the files and folders that are saved inside your own personal space in {productname-short}.
 
-. Bring the content of this {deliverable} inside your Jupyter environment:
+. Bring the content of this {deliverable} inside your JupyterLab environment:
 
 .. On the toolbar, click the *Git Clone* icon:
 +
@@ -36,7 +36,7 @@ https://github.com/rh-aiservices-bu/fraud-detection.git
 +
 image::workbenches/jupyter-git-modal.png[Git Modal, 200]
 
-.. Check the *Include submodules* option, and then click *Clone*.
+.. Select the *Include submodules* option, and then click *Clone*.
 
 .. In the file browser, double-click the newly-created *fraud-detection* folder.
 +
diff --git a/workshop/docs/modules/ROOT/pages/index.adoc b/workshop/docs/modules/ROOT/pages/index.adoc
@@ -8,22 +8,20 @@
 
 Welcome. In this {deliverable}, you learn how to incorporate data science and artificial intelligence and machine learning (AI/ML) into an OpenShift development workflow.
 
-You will use an example fraud detection model to complete the following tasks:
+You use an example fraud detection model to complete the following tasks in https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai[{productname-long}] without the need to install anything on your computer:
 
 * Explore a pre-trained fraud detection model by using a Jupyter notebook.
 * Deploy the model by using {productname-short} model serving.
 * Refine and train the model by using automated pipelines.
 * Learn how to train the model by using Ray, a distributed computing framework.
 
-You do not have to install anything on your own computer, thanks to https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai[{productname-long}].
-
 == About the example fraud detection model
 
 The example fraud detection model monitors credit card transactions for potential fraudulent activity. It analyzes the following credit card transaction details:
 
 * The geographical distance from the previous credit card transaction.
 * The price of the current transaction, compared to the median price of all the user's transactions.
-* Whether the user completed the transaction by using the hardware chip in the credit card,  entered a PIN number, or for an online purchase.
+* Whether the user completed the transaction by using the hardware chip in the credit card, by entering a PIN number, or by making an online purchase.
 
 Based on this data, the model outputs the likelihood of the transaction being fraudulent.
 
diff --git a/workshop/docs/modules/ROOT/pages/navigating-to-the-dashboard.adoc b/workshop/docs/modules/ROOT/pages/navigating-to-the-dashboard.adoc
@@ -7,7 +7,7 @@
 
 ** *If you are using the {org-name} Developer Sandbox*:
 +
-After you log in to the Sandbox, under *Available services*, in the {productname-long} card, click *Launch*.
+After you log in to the Sandbox, click *Getting Started* -> *Available services*, and then, in the {productname-long} card, click *Launch*.
 +
 image::projects/sandbox-rhoai-tile.png[{productname-short} dashboard link]
 
diff --git a/workshop/docs/modules/ROOT/pages/preparing-a-model-for-deployment.adoc b/workshop/docs/modules/ROOT/pages/preparing-a-model-for-deployment.adoc
@@ -18,7 +18,7 @@ image::projects/ds-project-connections.png[Data storage in workbench]
 
 .Procedure
 
-. In your Jupyter environment, open the `2_save_model.ipynb` file.
+. In your JupyterLab environment, open the `2_save_model.ipynb` file.
 
 . Follow the instructions in the notebook to make the model accessible in storage and save it in the portable ONNX format.
 
diff --git a/workshop/docs/modules/ROOT/pages/running-a-pipeline-generated-from-python-code.adoc b/workshop/docs/modules/ROOT/pages/running-a-pipeline-generated-from-python-code.adoc
@@ -5,7 +5,7 @@ In the previous section, you created a simple pipeline by using the GUI pipeline
 
 This {deliverable} does not describe the details of how to use the SDK. Instead, it provides the files for you to view and upload.
 
-. Optionally, view the provided Python code in your Jupyter environment by navigating to the `fraud-detection-notebooks` project's `pipeline` directory. It contains the following files:
+. Optionally, view the provided Python code in your JupyterLab environment by navigating to the `fraud-detection-notebooks` project's `pipeline` directory. It contains the following files:
 +
 * `7_get_data_train_upload.py` is the main pipeline code.
 * `get_data.py`, `train_model.py`, and `upload.py` are the three components of the pipeline.
diff --git a/workshop/docs/modules/ROOT/pages/running-a-script-to-install-storage.adoc b/workshop/docs/modules/ROOT/pages/running-a-script-to-install-storage.adoc
@@ -119,7 +119,7 @@ spec:
 image::projects/ds-project-connections.png[Connections for Fraud Detection]
 
 
-.Next steps
+.Next step
 
 If you want to complete the pipelines section of this {deliverable}, go to xref:enabling-data-science-pipelines.adoc[Enabling data science pipelines].
 
diff --git a/workshop/docs/modules/ROOT/pages/running-code-in-a-notebook.adoc b/workshop/docs/modules/ROOT/pages/running-code-in-a-notebook.adoc
@@ -13,7 +13,7 @@ This is a cell with some code:
 
 image::workbenches/cell_code.png[Jupyter Cell Code]
 
-Code cells contain Python code that you can run interactively. You can modify the code and then run it. The code does not run on your computer or in the browser, but directly in the environment that you are connected to, *{productname-long}* in our case.
+Code cells contain Python code that you can run interactively. You can modify the code and then run it. The code does not run on your computer or in the browser, but directly in the environment that you are connected to, *{productname-long}* in your case.
 
 You can run a code cell from the notebook interface or from the keyboard:
 
@@ -37,12 +37,12 @@ Now that you know the basics, give it a try.
 
 .Prerequisites
 
-* You have imported the {deliverable} files into your Jupyter environment as described in 
-xref:importing-files-into-jupyter.adoc[Importing the {deliverable} files into the Jupyter environment].
+* You have imported the {deliverable} files into your JupyterLab environment as described in 
+xref:importing-files-into-jupyter.adoc[Importing the {deliverable} files into the JupyterLab environment].
 
 .Procedure
 
-. In your Jupyter environment, locate the `0_sandbox.ipynb` file and double-click it to launch the notebook. The notebook opens in a new tab in the content section of the environment. 
+. In your JupyterLab environment, locate the `0_sandbox.ipynb` file and double-click it to launch the notebook. The notebook opens in a new tab in the content section of the environment. 
 +
 image::workbenches/jupyter-notebook-0.png[Notebook 0]
 
diff --git a/workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc b/workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc
@@ -1,15 +1,19 @@
 [id='setting-up-your-data-science-project']
 = Setting up your data science project
 
-Before you begin, make sure that you are logged in to *{productname-long}*.
+To implement a data science workflow, you must create a data science project (as described in the following procedure). Projects allow you and your team to organize and collaborate on resources within separated namespaces. From a project you can create multiple workbenches, each with their own IDE environment (for example, JupyterLab), and each with their own connections and cluster storage. In addition, the workbenches can share models and data with pipelines and model servers.
+
+.Prerequisites
+
+Before you begin, log in to *{productname-long}*.
 
 .Procedure
 
 . On the navigation menu, select *Data Science Projects*. This page lists any existing projects that you have access to. From this page, you can select an existing project (if any) or create a new one.
 +
 image::projects/launch-jupyter-link.png[Launch Jupyter link]
 +
-Note that it is possible to start a Jupyter notebook by clicking the *Launch standalone notebook server* link, selecting a notebook image, and clicking *Start server*. However, it would be a one-off Jupyter notebook run in isolation. To implement a data science workflow, you must create a data science project (as described in the following procedure). Projects allow you and your team to organize and collaborate on resources within separated namespaces. From a project you can create multiple workbenches, each with their own IDE environment (for example, JupyterLab), and each with their own connections and cluster storage. In addition, the workbenches can share models and data with pipelines and model servers.
+Note that it is possible to start a Jupyter notebook by clicking the *Launch standalone workbench* button, selecting a notebook image, and clicking *Start server*. However, it would be a one-off Jupyter notebook run in isolation.
 
 . If you are using your own OpenShift cluster, click *Create project*. 
 +
diff --git a/workshop/docs/modules/ROOT/pages/storing-data-with-connections.adoc b/workshop/docs/modules/ROOT/pages/storing-data-with-connections.adoc
diff --git a/workshop/docs/modules/ROOT/pages/testing-the-model-api.adoc b/workshop/docs/modules/ROOT/pages/testing-the-model-api.adoc

Original file line number	Diff line number	Diff line change
`@@ -1,13 +1,13 @@`
`1`	`1`	`[id='enabling-data-science-pipelines']`
`2`	`2`	`= Enabling data science pipelines`
`3`	`3`
`4`		`-NOTE: If you do not intend to complete the pipelines section of the workshop you can skip this step and move on to the next section, xref:creating-a-workbench.adoc[Create a Workbench].`
	`4`	`+NOTE: If you do not intend to complete the pipelines section of this {deliverable} you can skip this step and move on to the next section, xref:creating-a-workbench.adoc[Create a Workbench].`
`5`	`5`
`6`	`6`	`In this section, you prepare your {deliverable} environment so that you can use data science pipelines.`
`7`	`7`
`8`		`-In this {deliverable}, you implement an example pipeline by using the JupyterLab Elyra extension. With Elyra, you can create a visual end-to-end pipeline workflow that can be executed in OpenShift AI.`
	`8`	`+Later in this {deliverable}, you implement an example pipeline by using the JupyterLab Elyra extension. With Elyra, you can create a visual end-to-end pipeline workflow that can be executed in {productname-short}.`
`9`	`9`
`10`		`-.Prerequisite`
	`10`	`+.Prerequisites`
`11`	`11`
`12`	`12`	`* You have installed local object storage buckets and created connections, as described in xref:storing-data-with-connections.adoc[Storing data with connections].`
`13`	`13`
`@@ -34,7 +34,7 @@ image::projects/ds-project-create-pipeline-server-form.png[Selecting the Pipelin`
`34`	`34`	`You must wait until the pipeline configuration is complete before you continue and create your workbench. If you create your workbench before the pipeline server is ready, your workbench will not be able to submit pipelines to it.`
`35`	`35`	`====`
`36`	`36`	`+`
`37`		`-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.`
	`37`	`+If you have waited more than 5 minutes, and the pipeline server configuration does not complete, you can delete the pipeline server and create it again.`
`38`	`38`	`+`
`39`	`39`	`image::projects//ds-project-delete-pipeline-server.png[Delete pipeline server, 250]`
`40`	`40`	`+`
Original file line number	Diff line number	Diff line change
`@@ -1,9 +1,9 @@`
`1`	`1`	`[id='importing-files-into-jupyter']`
`2`		`-= Importing the {deliverable} files into the Jupyter environment`
	`2`	`+= Importing the {deliverable} files into the JupyterLab environment`
`3`	`3`
`4`	`4`	`:git-version: main`
`5`	`5`
`6`		`-The Jupyter environment is a web-based environment, but everything you do inside it happens on {productname-long} and is powered by the OpenShift cluster. This means that, without having to install and maintain anything on your own computer, and without disposing of valuable local resources such as CPU, GPU and RAM, you can conduct your Data Science work in this powerful and stable managed environment.`
	`6`	`+The JupyterLab environment is a web-based environment, but everything you do inside it happens on {productname-long} and is powered by the OpenShift cluster. This means that, without having to install and maintain anything on your own computer, and without using valuable local resources such as CPU, GPU and RAM, you can conduct your data science work in this powerful and stable managed environment.`
`7`	`7`
`8`	`8`	`.Prerequisites`
`9`	`9`
`@@ -15,11 +15,11 @@ You created a workbench, as described in xref:creating-a-workbench.adoc[Creating`
`15`	`15`	`+`
`16`	`16`	`image::workbenches/ds-project-workbench-open.png[Open workbench]`
`17`	`17`	`+`
`18`		`-Your Jupyter environment window opens.`
	`18`	`+Your JupyterLab environment window opens.`
`19`	`19`	`+`
`20`	`20`	`This file-browser window shows the files and folders that are saved inside your own personal space in {productname-short}.`
`21`	`21`
`22`		`-. Bring the content of this {deliverable} inside your Jupyter environment:`
	`22`	`+. Bring the content of this {deliverable} inside your JupyterLab environment:`
`23`	`23`
`24`	`24`	`.. On the toolbar, click the Git Clone icon:`
`25`	`25`	`+`
`@@ -36,7 +36,7 @@ https://github.com/rh-aiservices-bu/fraud-detection.git`
`36`	`36`	`+`
`37`	`37`	`image::workbenches/jupyter-git-modal.png[Git Modal, 200]`
`38`	`38`
`39`		`-.. Check the Include submodules option, and then click Clone.`
	`39`	`+.. Select the Include submodules option, and then click Clone.`
`40`	`40`
`41`	`41`	`.. In the file browser, double-click the newly-created fraud-detection folder.`
`42`	`42`	`+`
Original file line number	Diff line number	Diff line change
`@@ -7,7 +7,7 @@`
`7`	`7`
`8`	`8`	`** If you are using the {org-name} Developer Sandbox:`
`9`	`9`	`+`
`10`		`-After you log in to the Sandbox, under Available services, in the {productname-long} card, click Launch.`
	`10`	`+After you log in to the Sandbox, click Getting Started -> Available services, and then, in the {productname-long} card, click Launch.`
`11`	`11`	`+`
`12`	`12`	`image::projects/sandbox-rhoai-tile.png[{productname-short} dashboard link]`
`13`	`13`
Original file line number	Diff line number	Diff line change
`@@ -5,7 +5,7 @@ In the previous section, you created a simple pipeline by using the GUI pipeline`
`5`	`5`
`6`	`6`	`This {deliverable} does not describe the details of how to use the SDK. Instead, it provides the files for you to view and upload.`
`7`	`7`
`8`		-. Optionally, view the provided Python code in your Jupyter environment by navigating to the `fraud-detection-notebooks` project's `pipeline` directory. It contains the following files:
	`8`	+. Optionally, view the provided Python code in your JupyterLab environment by navigating to the `fraud-detection-notebooks` project's `pipeline` directory. It contains the following files:
`9`	`9`	`+`
`10`	`10`	* `7_get_data_train_upload.py` is the main pipeline code.
`11`	`11`	* `get_data.py`, `train_model.py`, and `upload.py` are the three components of the pipeline.