doc-update-ptnfly6 update images and adoc files to reflect Patternfly… (

#63)

* doc-update-ptnfly6 update images and adoc files to reflect Patternfly 6 UI changes

* doc-update-ptnfly6 - address peer review comments

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

@@ -65,31 +65,33 @@ image::pipelines/wb-pipeline-node-1.png[Select Node 1, 150]
 
 . Scroll down to the *File Dependencies* section and then click *Add*.
 +
-image::pipelines/wb-pipeline-node-1-file-dep.png[Add File Dependency, 200]
+image::pipelines/wb-pipeline-node-1-file-dep.png[Add File Dependency, 500]
 
 . Set the value to `data/*.csv` which contains the data to train your model.
 
-. Select the *Include Subdirectories* option and then click *Add*.
+. Select the *Include Subdirectories* option.
 +
-image::pipelines/wb-pipeline-node-1-file-dep-form.png[Set File Dependency Value, 500]
+image::pipelines/wb-pipeline-node-1-file-dep-form.png[Set File Dependency Value, 300]
 
-. Save the pipeline.
+. *Save* the pipeline.
 
 == Create and store the ONNX-formatted output file
 
 In node 1, the notebook creates the `models/fraud/1/model.onnx` file. In node 2, the notebook uploads that file to the S3 storage bucket. You must set `models/fraud/1/model.onnx` file as the output file for both nodes.
 
-. Select node 1 and then select the *Node Properties* tab.
+. Select node 1.
+
+. Select the *Node Properties* tab.
 
 . Scroll down to the *Output Files* section, and then click *Add*.
 
-. Set the value to `models/fraud/1/model.onnx` and then click *Add*.
+. Set the value to `models/fraud/1/model.onnx`.
 +
 image::pipelines/wb-pipeline-node-1-file-output-form.png[Set file dependency value, 400]
 
-. Repeat steps 1-3 for node 2.
+. Repeat steps 2-4 for node 2.
 
-. Save the pipeline.
+. *Save* the pipeline.
 
 == Configure the connection to the S3 storage bucket
 
@@ -144,7 +146,7 @@ image::pipelines/wb-pipeline-add-kube-secret.png[Add Kubernetes Secret]
 ** *Secret Name*: `aws-connection-my-storage`
 ** *Secret Key*: `AWS_ACCESS_KEY_ID`
 +
-image::pipelines/wb-pipeline-kube-secret-form.png[Secret Form, 300]
+image::pipelines/wb-pipeline-kube-secret-form.png[Secret Form, 400]
 
 . Repeat Step 2 for each of the following Kubernetes secrets:
 
@@ -185,15 +187,15 @@ NOTE: If `Data Science Pipeline` is not available as a runtime configuration, yo
 
 . Return to your data science project and expand the newly created pipeline.
 +
-image::pipelines/dsp-pipeline-complete.png[Set File Dependency Value, 800]
+image::pipelines/dsp-pipeline-complete.png[New pipeline expanded, 800]
 
-. Click the action menu (⋮) and then select *View runs* from the list.
+. Click *View runs*.
 +
-image::pipelines/dsp-view-run.png[Set File Dependency Value, 800]
+image::pipelines/dsp-view-run.png[View runs for selected pipeline, 500]
 
-. Click on your run and then view the pipeline run in progress.
+. Click your run and then view the pipeline run in progress.
 +
-image::pipelines/pipeline-run-complete.png[Set File Dependency Value, 800]
+image::pipelines/pipeline-run-complete.png[Pipeline run progress, 800]
 
 The result should be a `models/fraud/1/model.onnx` file in your S3 bucket which you can serve, just like you did manually in the xref:preparing-a-model-for-deployment.adoc[Preparing a model for deployment] section.
 

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

@@ -32,25 +32,17 @@ image::workbenches/create-workbench-form-image.png[Workbench image, 600]
 +
 image::workbenches/create-workbench-form-size.png[Workbench size, 600]
 
-. Leave the *Accelerator* field with the default `None` selection.
-+ 
-image::workbenches/create-workbench-form-accelerator.png[Workbench accelerator, 600]
-
 . Leave the default environment variables and storage options.
 +
 image::workbenches/create-workbench-form-env-storage.png[Workbench storage, 600]
 
-. Under *Connections*, select *Use a connection*.
-+
-image::workbenches/create-workbench-form-use-data-connection.png[Use connection, 600]
+. For *Connections*, click *Attach existing connection*.
 
-. Select *Use existing connection*, and then select `My Storage` (the object storage that you configured previously) from the list.
+. Select `My Storage` (the object storage that you configured previously) and then click *Attach*.
 +
 image::workbenches/create-workbench-form-data-connection.png[Connection form, 600]
 
-. Click the *Create workbench* button.
-+
-image::workbenches/create-workbench-form-button.png[Create workbench button, 150]
+. Click *Create workbench*.
 
 .Verification
 
@@ -60,7 +52,7 @@ 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, 250]
+image::workbenches/ds-project-workbench-list-edit.png[Workbench list edit, 350]
 
 
 .Next step

workshop/docs/modules/ROOT/pages/creating-connections-to-storage.adoc

@@ -1,4 +1,4 @@
-[id='creating-data-connections-to-storage']
+[id='creating-connections-to-storage']
 = Creating connections to your own S3-compatible object storage
 
 If you have existing S3-compatible storage buckets that you want to use for this {deliverable}, you must create a connection to one storage bucket for saving your data and models. If you want to complete the pipelines section of this {deliverable}, create another connection to a different storage bucket for saving pipeline artifacts.
@@ -23,19 +23,17 @@ If you don't have this information, contact your storage administrator.
 
 .. In the {productname-short} dashboard, navigate to the page for your data science project.
 
-.. Click the *Connections* tab, and then click *Add connection*.
+.. Click the *Connections* tab, and then click *Create connection*.
 +
 image::projects/ds-project-add-dc.png[Add connection]
 
 .. In the *Add connection* modal, for the *Connection type* select *S3 compatible object storage - v1*.
 
 .. Complete the *Add connection* form and name your connection *My Storage*. This connection is for saving your personal work, including data and models. 
 +
-NOTE: Skip the *Connected workbench* item. You add connections to a workbench in a later section.
-+
 image::projects/ds-project-my-storage-form.png[Add my storage form, 500]
 
-.. Click *Add connection*.
+.. Click *Create*.
 
 . Create a connection for saving pipeline artifacts:
 +
@@ -45,18 +43,16 @@ NOTE: If you do not intend to complete the pipelines section of the {deliverable
 
 .. Complete the form and name your connection *Pipeline Artifacts*.
 +
-NOTE: Skip the *Connected workbench* item. You add connections to a workbench in a later section.
-+
 image::projects/ds-project-pipeline-artifacts-form.png[Add pipeline artifacts form, 500]
 
-.. Click *Add connection*.
+.. Click *Create*.
 
 
 .Verification
 
 In the *Connections* tab for the project, check to see that your connections are listed.
 
-image::projects/ds-project-dc-list.png[List of project connections, 500]
+image::projects/ds-project-connections.png[List of project connections, 500]
 
 
 .Next steps

workshop/docs/modules/ROOT/pages/deploying-a-model-multi-model-server.adoc

@@ -15,7 +15,7 @@ image::model-serving/ds-project-model-list-add.png[Models]
 +
 NOTE: Depending on how model serving has been configured on your cluster, you might see only one model serving platform option.
 
-. In the *Multi-model serving platform* tile, click *Add model server*.
+. In the *Multi-model serving platform* tile, click *Select multi-model*.
 
 . In the form, provide the following values:
 .. For *Model server name*, type a name, for example `Model Server`.
@@ -28,7 +28,7 @@ image::model-serving/create-model-server-form.png[Create model server form, 400]
 
 . In the *Models and model servers* list, next to the new model server, click *Deploy model*.
 +
-image::model-serving/ds-project-workbench-list-deploy.png[Create model server form]
+image::model-serving/ds-project-workbench-list-deploy.png[Create model server form, 500]
 
 . In the form, provide the following values:
 .. For *Model deployment name*, type `fraud`.
@@ -37,15 +37,15 @@ image::model-serving/ds-project-workbench-list-deploy.png[Create model server fo
 .. Type the path that leads to the version folder that contains your model file: `models/fraud`
 .. Leave the other fields with the default settings.
 +
-image::model-serving/deploy-model-form-mm.png[Deploy model from for multi-model serving, 400]
+image::model-serving/deploy-model-form-mm.png[Deploy model from for multi-model serving, 500]
 
 . Click *Deploy*.
 
 .Verification
 
 Notice the loading symbol under the *Status* section. The symbol changes to a green checkmark when the deployment completes successfully. 
 
-image::model-serving/ds-project-model-list-status-mm.png[Deployed model status]
+image::model-serving/ds-project-model-list-status.png[Deployed model status, 350]
 
 
 .Next step

workshop/docs/modules/ROOT/pages/deploying-a-model-single-model-server.adoc

@@ -16,7 +16,7 @@ image::model-serving/ds-project-model-list-add.png[Models]
 +
 NOTE: Depending on how model serving has been configured on your cluster, you might see only one model serving platform option.
 
-. In the *Single-model serving platform* tile, click *Deploy model*.
+. In the *Single-model serving platform* tile, click *Select single-model*.
 . In the form, provide the following values:
 .. For *Model deployment name*, type `fraud`.
 .. For *Serving runtime*, select `OpenVINO Model Server`.
@@ -33,7 +33,7 @@ image::model-serving/deploy-model-form-sm.png[Deploy model form for single-model
 
 Notice the loading symbol under the *Status* section. The symbol changes to a green checkmark when the deployment completes successfully.
 
-image::model-serving/ds-project-model-list-status-sm.png[Deployed model status]
+image::model-serving/ds-project-model-list-status.png[Deployed model status, 350]
 
 .Next step
 

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

@@ -36,7 +36,7 @@ You must wait until the pipeline configuration is complete before you continue a
 +
 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, 150]
+image::projects//ds-project-delete-pipeline-server.png[Delete pipeline server, 250]
 +
 You can also ask your {productname-short} administrator to verify that self-signed certificates are added to your cluster as described in link:https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2-latest/html/installing_and_uninstalling_openshift_ai_self-managed/working-with-certificates_certs[Working with certificates].
 
@@ -45,7 +45,7 @@ You can also ask your {productname-short} administrator to verify that self-sign
 . 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, 150]
+image::projects/ds-project-pipeline-server-view.png[View pipeline server configuration menu, 250]
 +
 An information box opens and displays the object storage connection information for the pipeline server.
 

workshop/docs/modules/ROOT/pages/navigating-to-the-dashboard.adoc

@@ -25,7 +25,7 @@ The {productname-short} dashboard shows the *Home* page.
 
 NOTE: You can navigate back to the OpenShift console by clicking the application launcher to access the OpenShift console.
 
-image::projects/ds-console-ocp-tile.png[OCP console link, 400]
+image::projects/ds-console-ocp-tile.png[OCP console link, 300]
 
 For now, stay in the {productname-short} dashboard.
 

workshop/docs/modules/ROOT/pages/preparing-a-model-for-deployment.adoc

@@ -14,7 +14,7 @@ To prepare a model for deployment, you must complete the following tasks:
 * You created the `My Storage` connection and have added it
 to your workbench.
 +
-image::projects/ds-project-dc-list.png[Data storage in workbench]
+image::projects/ds-project-connections.png[Data storage in workbench]
 
 .Procedure
 

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

@@ -18,8 +18,6 @@ For your convenience, the output of the `build.sh` script is provided in the `7_
 . Upload the `7_get_data_train_upload.yaml` file to {productname-short}.
 
 .. In the {productname-short} dashboard, navigate to your data science project page. Click the *Pipelines* tab and then click *Import pipeline*.
-+
-image::pipelines/dsp-pipeline-import.png[]
 
 .. Enter values for *Pipeline name* and *Pipeline description*.
 
@@ -29,25 +27,23 @@ image::pipelines/dsp-pipline-import-upload.png[]
 
 .. Click *Import pipeline* to import and save the pipeline.
 +
-The pipeline shows in the list of pipelines.
-
-. Expand the pipeline item, click the action menu (⋮), and then select *View runs*.
+The pipeline shows in graphic view.
 +
-image::pipelines/dsp-pipline-view-runs.png[]
+image::pipelines/python-pipeline-graph.png[]
 
-. Click *Create run*.
+. Select *Actions* -> *Create run*.
 
 . On the *Create run* page, provide the following values:
-.. For *Experiment*, leave the default `Default` value. 
+.. For *Experiment*, leave the value as `Default`. 
 .. For *Name*, type any name, for example `Run 1`.
 .. For *Pipeline*, select the pipeline that you uploaded.
 +
 You can leave the other fields with their default values.
 +
 image::pipelines/pipeline-create-run-form.png[Create Pipeline Run form]
 
-. Click *Create* to create the run.
+. Click *Create run* to create the run.
 +
-A new run starts immediately. The run details page shows a pipeline created in Python that is running in {productname-short}.
+A new run starts immediately.
 +
-image::pipelines/pipeline-run-in-progress.png[]
+image::pipelines/pipeline-run-in-progress.png[New pipeline run, 400]

workshop/docs/modules/ROOT/pages/testing-the-model-api.adoc

@@ -10,9 +10,9 @@ Now that you've deployed the model, you can test its API endpoints.
 
 . Take note of the model's Inference endpoint URL. You need this information when you test the model API.
 +
-image::model-serving/ds-project-model-inference-endpoint.png[Model inference endpoint]
-+
 If the *Inference endpoint* field contains an *Internal endpoint details* link, click the link to open a text box that shows the URL details, and then take note of the *restUrl* value.
++
+image::model-serving/ds-project-model-inference-endpoint.png[Model inference endpoint, 300]
 
 . Return to the Jupyter environment and try out your new endpoint.
 +