RHOAIENG-27677 - Address code rabbit feedback (#888)

Author: eturner24

modules/about-workbench-images.adoc

@@ -4,7 +4,7 @@
 = About workbench images
 
 [role="_abstract"]
-A workbench image is optimized with the tools and libraries that you need for model development. You can use the provided workbench images or an {productname-short} administrator can create custom workbench images adapted to your needs.
+A workbench image is preinstalled with the tools and libraries that you need for model development. You can use the provided workbench images or an {productname-short} administrator can create custom workbench images adapted to your needs.
 
 To provide a consistent, stable platform for your model development, many provided workbench images contain the same version of Python. Most workbench images available on {productname-short} are pre-built and ready for you to use immediately after {productname-short} is installed or upgraded. 
 
@@ -105,8 +105,8 @@ endif::[]
 | Use the ROCm workbench image to run AI and machine learning workloads on AMD GPUs in {productname-short}. It includes ROCm libraries and tools optimized for high-performance GPU acceleration, supporting custom AI workflows and data processing tasks. Use this image integrating additional frameworks or dependencies tailored to your specific AI development needs.
 
 | ROCm-PyTorch
-| Use the ROCm-PyTorch workbench image to optimize PyTorch workloads on AMD GPUs in {productname-short}. It includes ROCm-accelerated PyTorch libraries, enabling efficient deep learning training, inference, and experimentation. This image is designed for data scientists working with PyTorch-based workflows, offering integration with GPU scheduling.
+| Use the ROCm-PyTorch workbench image to run PyTorch workloads on AMD GPUs in {productname-short}. It includes ROCm-accelerated PyTorch libraries, enabling efficient deep learning training, inference, and experimentation. This image is designed for data scientists working with PyTorch-based workflows, offering integration with GPU scheduling.
 
 | ROCm-TensorFlow
-| Use the ROCm-TensorFlow workbench image to optimize TensorFlow workloads on AMD GPUs in {productname-short}. It includes ROCm-accelerated TensorFlow libraries to support high-performance deep learning model training and inference. This image simplifies TensorFlow development on AMD GPUs and integrates with {productname-short} for resource scaling and management.
+| Use the ROCm-TensorFlow workbench image to run TensorFlow workloads on AMD GPUs in {productname-short}. It includes ROCm-accelerated TensorFlow libraries to support high-performance deep learning model training and inference. This image simplifies TensorFlow development on AMD GPUs and integrates with {productname-short} for resource scaling and management.
 |===

modules/adding-workbench-pod-tolerations.adoc

@@ -41,7 +41,7 @@ endif::[]
 
 .Verification
 
-. In the {openshift-platform} console, for a pod that is running, click *Workloads* -> *Pods*. Otherwise, for a pod that is stopped, click *Workloads* -> *StatefulSet*.
+. In the {openshift-platform} console, select your data science project, then click *Workloads* -> *StatefulSet*. You can see how many pods are running, either zero or one, depending on whether your workbench is currently started or stopped. 
 . Search for your workbench pod name and then click the name to open the pod details page.
 . Confirm that the assigned *Node* and *Tolerations* are correct.
 

modules/configuring-the-default-pvc-size-for-your-cluster.adoc

@@ -13,7 +13,7 @@ To configure how resources are claimed within your {productname-short} cluster,
 .Prerequisites
 * You have logged in to {productname-short} as a user with {productname-short} administrator privileges. 
 
-NOTE: Changing the PVC setting restarts the workbench pod and makes Jupyter unavailable for up to 30 seconds. As a workaround, it is recommended that you perform this action outside of your organization's typical working day.
+NOTE: Changing the PVC setting restarts the workbench pod and makes it unavailable for up to 30 seconds. As a workaround, it is recommended that you perform this action outside of your organization's typical working day.
 
 .Procedure
 . From the {productname-short} dashboard, click *Settings* -> *Cluster settings*.

modules/copying-files-between-buckets.adoc

@@ -13,7 +13,7 @@ To copy files between buckets in your object store from your workbench, use the
 * You know the key of the source file that you want to copy, and the bucket that the file is stored in.
 
 .Procedure
-. In the notebook file, locate the following instructions to copy files between buckets:
+. In the notebook, locate the following instructions to copy files between buckets:
 +
 [source]
 ----

modules/creating-a-custom-image-from-your-own-image.adoc

@@ -68,7 +68,7 @@ COPY --chmod=775 os-packages.txt ./
 WORKDIR /opt/app-root/src
 ----
 +
-When a user launches a workbench from the {productname-short} *Applications* → *Enabled* page, the "personal" volume of the user is mounted at `/opt/app-root/src`. Because this location is not configurable, when you build your custom image, you must specify this default location for persisted data.
+When a user launches a workbench from the {productname-short} *Applications* → *Enabled* page, the personal volume of the user is mounted in the user's HOME directory (`/opt/app-root/src`). Because this location is not configurable, when you build your custom image, you must specify this default location for persisted data.
 
 * Fix permissions to support PIP (the package manager for Python packages) in OpenShift environments. Add the following command to your custom image (if needed, change `python3.9` to the Python version that you are using):
 +

modules/creating-a-runtime-configuration.adoc

@@ -51,7 +51,7 @@ You can obtain the data science pipelines API endpoint from the *Data science pi
 +
 [IMPORTANT]
 ====
-If you created a notebook directly from the *Start basic workbench* tile on the dashboard, select `EXISTING_BEARER_TOKEN` from the *Authentication Type* list.
+If you created a workbench directly from the *Start basic workbench* tile on the dashboard, select `EXISTING_BEARER_TOKEN` from the *Authentication Type* list.
 ====
 ... In the *Data Science Pipelines API Endpoint Username* field, enter the user name required for the authentication type.
 ... In the *Data Science Pipelines API Endpoint Password Or Token*, enter the password or token required for the authentication type.

modules/starting-a-basic-workbench.adoc

@@ -32,7 +32,7 @@ endif::[]
 If your credentials are accepted, the *Workbench control panel* opens displaying the *Start a basic workbench* page.
 . Start a basic workbench.
 
-.. In the *Workbench image* section, select the workbench image to use for your server. 
+.. In the *Workbench image* section, select the workbench image to use for your workbench. 
 +
 Different workbench images have different packages installed by default. Click the help icon (?) next to a workbench image name to view a list of its included packages.
 
@@ -42,7 +42,7 @@ Different workbench images have different packages installed by default. Click t
 --
 When a new version of a workbench image is released, the previous version remains available and supported on the cluster. This gives you time to migrate your work to the latest version of the workbench image.
 --
-.. From the *Container size* list, select a suitable container size for your server.
+.. From the *Container size* list, select a suitable container size for your workbench.
 .. Optional: From the *Accelerator* list, select an accelerator. 
 .. If you selected an accelerator in the preceding step, specify the number of accelerators to use.
 +

modules/troubleshooting-common-problems-in-workbenches-for-administrators.adoc

@@ -120,9 +120,9 @@ endif::[]
 * Work with the user to identify files that can be deleted from the `/opt/app-root/src` directory on their workbench to free up their existing storage space.
 
 [NOTE]
---
+====
 When you delete files using the JupyterLab file explorer, the files move to the hidden `/opt/app-root/src/.local/share/Trash/files` folder in the persistent storage for the workbench. To free up storage space for workbenches, you must permanently delete these files.
--- 
+====
 
 // [role='_additional-resources']
 // == Additional resources

modules/updating-your-project-with-changes-from-a-remote-git-repository.adoc

@@ -7,11 +7,11 @@
 You can pull changes made by other users into your data science project from a remote Git repository.
 
 .Prerequisites
+* You have a launched and running workbench based on a JupyterLab image.
+* You have credentials for logging in to Jupyter.
 * You have configured the remote Git repository.
-* You have imported the Git repository into JupyterLab, and the contents of the repository are visible in the file browser in JupyterLab.
 * You have permissions to pull files from the remote Git repository to your local repository.
-* You have credentials for logging in to Jupyter.
-* You have a launched and running workbench based on a JupyterLab image.
+* You have imported the Git repository into JupyterLab, and the contents of the repository are visible in the file browser in JupyterLab.
 
 .Procedure
 . In the JupyterLab interface, click the *Git* button (image:images/jupyter-git-sidebar.png[Git button]).