doc-update: notebook #9 edit and new screen-captures (#108)

Author: MelissaFlinn

9_distributed_training_kfto.ipynb

@@ -153,17 +153,11 @@
     "\n",
     "The model training script loads and distributes the training data set among nodes, performs distributed training, evaluates by using the test data set, and exports the trained model to ONNX format and uploads it to the S3 bucket that is specified in the provided connection.\n",
     "\n",
-    "*Important note:* If one of the following is true:\n",
+    "*Important note:* If the following is true, you must edit the script in the next Python cell to uncomment the label declaration in the `create_job` function.\n",
     "\n",
     "* You are not using the Red Hat Sandbox test environment. \n",
     "\n",
-    "* The Kueue component is enabled for OpenShift AI and you have created all Kueue related resources (`ResourceFlavor`, `ClusterQueue`, and `LocalQueue`) and set the `local_queue_name` to \"local-queue\", as described in the _Setting up Kueue resources_ section of this Fraud Detection workshop/tutorial.\n",
-    "\n",
-    "You must make the following edits to the script in the next Python cell:\n",
-    "\n",
-    "* Provide the `LocalQueue` name.\n",
-    "\n",
-    "* Uncomment the label declaration in the `create_job` function."
+    "* The Kueue component is enabled for OpenShift AI and you have created all Kueue related resources (`ResourceFlavor`, `ClusterQueue`, and `LocalQueue`) and set the `local_queue_name` to \"local-queue\", as described in the _Setting up Kueue resources_ section of this Fraud Detection workshop/tutorial."
    ]
   },
   {
@@ -202,7 +196,7 @@
     "    },\n",
     "    base_image=\"quay.io/modh/training:py311-cuda124-torch251\",\n",
     "    # If the Kueue component is enabled and you have created the Kueue-related resources (ResourceFlavor, ClusterQueue and LocalQueue), then uncomment the following line to add the queue-name label:\n",
-    "    # labels={\"kueue.x-k8s.io/queue-name\": local-queue},\n",
+    "    # labels={\"kueue.x-k8s.io/queue-name\": \"local-queue\"},\n",
     "    env_vars=[\n",
     "        V1EnvVar(name=\"AWS_ACCESS_KEY_ID\", value=os.environ.get(\"AWS_ACCESS_KEY_ID\")),\n",
     "        V1EnvVar(name=\"AWS_S3_BUCKET\", value=os.environ.get(\"AWS_S3_BUCKET\")),\n",

workshop/docs/modules/ROOT/assets/images/distributed/jupyter-notebook.png

@@ -18,7 +18,7 @@ To explore the pipeline editor, complete the steps in the following procedure to
 
 . Open your workbench's JupyterLab environment. If the launcher is not visible, click *+* to open it.
 +
-image::pipelines/wb-pipeline-launcher.png[Pipeline buttons]
+image::pipelines/wb-pipeline-launcher.png[Pipeline buttons, 400]
 
 . Click *Pipeline Editor*.
 +