odh-3274 update the getting started guide (#303)

* odh-3274 update the getting started guide

* odh-3724-1 cleaning up files and some links

* odh-3274-1 addressed peer review comments

* odh-3274 more peer review comments

getting-started-with-open-data-hub.adoc

@@ -15,36 +15,16 @@ include::_artifacts/document-attributes-global.adoc[]
 
 = Getting started with Open Data Hub
 
-include::modules/logging-in-to-open-data-hub.adoc[leveloffset=+1]
+include::modules/overview-for-getting-started.adoc[leveloffset=+1]
 
-include::modules/the-open-data-hub-user-interface.adoc[leveloffset=+1]
-
-include::modules/notifications-in-open-data-hub.adoc[leveloffset=+1]
+include::modules/logging-in.adoc[leveloffset=+1]
 
 include::modules/creating-a-data-science-project.adoc[leveloffset=+1]
 
-include::modules/creating-a-project-workbench.adoc[leveloffset=+1]
-
-include::modules/starting-a-jupyter-notebook-server.adoc[leveloffset=+2]
-
-include::modules/options-for-notebook-server-environments.adoc[leveloffset=+2]
-
-include::modules/tutorials-for-data-scientists.adoc[leveloffset=+1]
-
-include::modules/accessing-tutorials.adoc[leveloffset=+2]
-
-include::modules/configuring-your-ide.adoc[leveloffset=+1]
-
-include::modules/enabling-applications-connected.adoc[leveloffset=+1]
-
-include::modules/disabling-applications-connected.adoc[leveloffset=+1]
-
-include::modules/removing-disabled-applications.adoc[leveloffset=+2]
+include::modules/creating-a-workbench-select-ide.adoc[leveloffset=+1]
 
-include::assemblies/support-requirements-and-limitations.adoc[leveloffset=+1]
+include::modules/about-workbench-images.adoc[leveloffset=+2]
 
-include::modules/common-questions.adoc[leveloffset=+1]
+include::modules/creating-a-project-workbench.adoc[leveloffset=+2]
 
-// [role='_additional-resources']
-// == Additional resources
-// * link to any relevant resources
+include::modules/next-steps-getting-started.adoc[leveloffset=+1]

modules/about-workbench-images.adoc

@@ -0,0 +1,104 @@
+:_module-type: CONCEPT
+
+[id="about-workbench-images_{context}"]
+= About workbench images
+
+[role="_abstract"]
+A workbench image (sometimes referred to as a notebook 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} admin user 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. 
+
+ifndef::upstream[]
+For information about Red Hat support of workbench images and packages, see link:https://access.redhat.com/articles/rhoai-supported-configs[{productname-long}: Supported Configurations].
+endif::[]
+
+{productname-long} contains the following notebook images that are available by default.
+
+ifndef::upstream[]
+[IMPORTANT]
+====
+Notebook images denoted with `(Technology Preview)` in this table are not supported with {org-name} production service level agreements (SLAs) and might not be functionally complete. {org-name} does not recommend using Technology Preview features in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of {org-name} Technology Preview features, see Technology Preview Features Support Scope.
+====
+endif::[]
+
+.Default notebook images
+[cols="1,5"]
+|===
+| Image name | Description
+
+| CUDA
+| If you are working with compute-intensive data science models that require GPU support, use the Compute Unified Device Architecture (CUDA) notebook image to gain access to the NVIDIA CUDA Toolkit. Using this toolkit, you can optimize your work by using GPU-accelerated libraries and optimization tools.
+
+| Standard Data Science
+| Use the Standard Data Science notebook image for models that do not require TensorFlow or PyTorch. This image contains commonly-used libraries to assist you in developing your machine learning models.
+
+| TensorFlow
+| TensorFlow is an open source platform for machine learning. With TensorFlow, you can build, train and deploy your machine learning models. TensorFlow contains advanced data visualization features, such as computational graph visualizations. It also allows you to easily monitor and track the progress of your models.
+
+| PyTorch
+| PyTorch is an open source machine learning library optimized for deep learning. If you are working with computer vision or natural language processing models, use the Pytorch notebook image.
+
+| Minimal Python
+| If you do not require advanced machine learning features, or additional resources for compute-intensive data science work, you can use the Minimal Python image to develop your models.
+
+| TrustyAI
+| Use the TrustyAI notebook image to leverage your data science work with model explainability, tracing, and accountability, and runtime monitoring.
+
+| HabanaAI
+| The HabanaAI notebook image optimizes high-performance deep learning (DL) with Habana Gaudi devices. Habana Gaudi devices accelerate DL training workloads and maximize training throughput and efficiency.
+
+ifndef::upstream[]
+| code-server (Technology Preview)
+endif::[]
+ifdef::upstream[]
+| code-server 
+endif::[]
+a| With the code-server notebook image, you can customize your notebook environment to meet your needs using a variety of extensions to add new languages, themes, debuggers, and connect to additional services. Enhance the efficiency of your data science work with syntax highlighting, auto-indentation, and bracket matching, as well as an automatic task runner for seamless automation. See link:https://github.com/coder/code-server[code-server in GitHub] for more information. +
+
+NOTE: Elyra-based pipelines are not available with the code-server notebook image.
+
+ifdef::upstream[]
+| RStudio Server 
+a| Use the RStudio Server notebook image to access the RStudio IDE, an integrated development environment for R, a programming language for statistical computing and graphics.
+See link:https://posit.co/products/open-source/rstudio-server/[the RStudio Server site] for more information. +
+endif::[]
+ifndef::upstream[]
+ifdef::cloud-service[]
+| RStudio Server (Technology preview)
+a| Use the RStudio Server notebook image to access the RStudio IDE, an integrated development environment for R, a programming language for statistical computing and graphics.
+See link:https://posit.co/products/open-source/rstudio-server/[the RStudio Server site] for more information. +
+
+To use the *RStudio Server* notebook image, you must first build it by creating a secret and triggering the BuildConfig, and then enable it in the {productname-short} UI by editing the `rstudio-rhel9` image stream. See link:{rhoaidocshome}{default-format-url}/working-with-connected-applications/working-in-rstudio-server_connected-apps#building_the_rstudio_server_connected-apps[Building the RStudio Server notebook images] for more information.
+
+[IMPORTANT] 
+====
+*Disclaimer:* +
+{org-name} supports managing workbenches in {productname-short}. However, {org-name} does not provide support for the RStudio software. RStudio Server is available through link:https://rstudio.org/[https://rstudio.org/] and is subject to their licensing terms. Review their licensing terms before you use this sample workbench.
+====
+endif::[]
+endif::[]
+
+ifdef::upstream[]
+| CUDA - RStudio Server
+a| Use the CUDA - RStudio Server notebook image to access the RStudio IDE and NVIDIA CUDA Toolkit. RStudio is an integrated development environment for R, a programming language for statistical computing and graphics. With the NVIDIA CUDA toolkit, you can optimize your work using GPU-accelerated libraries and optimization tools.
+See link:https://posit.co/products/open-source/rstudio-server/[the RStudio Server site] for more information. +
+endif::[]
+
+ifndef::upstream[]
+ifdef::cloud-service[]
+| CUDA - RStudio Server (Technology preview)
+a| Use the CUDA - RStudio Server notebook image to access the RStudio IDE and NVIDIA CUDA Toolkit. RStudio is an integrated development environment for R, a programming language for statistical computing and graphics. With the NVIDIA CUDA toolkit, you can optimize your work using GPU-accelerated libraries and optimization tools.
+See link:https://posit.co/products/open-source/rstudio-server/[the RStudio Server site] for more information. 
+
+To use the *CUDA - RStudio Server* notebook image, you must first build it by creating a secret and triggering the BuildConfig, and then enable it in the {productname-short} UI by editing the `cuda-rstudio-rhel9` image stream. See link:{rhoaidocshome}{default-format-url}/working-with-connected-applications/working-in-rstudio-server_connected-apps#building_the_rstudio_server_connected-apps[Building the RStudio Server notebook images] for more information. +
+
+[IMPORTANT] 
+====
+*Disclaimer:* +
+{org-name} supports managing workbenches in {productname-short}. However, {org-name} does not provide support for the RStudio software. RStudio Server is available through link:https://rstudio.org/[https://rstudio.org/] and is subject to their licensing terms. Review their licensing terms before you use this sample workbench. 
+
+The *CUDA - RStudio Server* notebook image contains NVIDIA CUDA technology. CUDA licensing information is available at link:https://docs.nvidia.com/cuda/[https://docs.nvidia.com/cuda/]. Review their licensing terms before you use this sample workbench.
+====
+endif::[]
+endif::[]
+|===

modules/accessing-notebook-servers-owned-by-other-users.adoc

@@ -11,19 +11,19 @@ Administrators can access notebook servers that are owned by other users to corr
 ifdef::upstream[]
 * You are part of the {openshift-platform} administrator group which requires  the `cluster-admin` role on {openshift-platform}. For more information, see link:https://access.redhat.com/documentation/en-us/openshift_container_platform/{ocp-latest-version}/html/authentication_and_authorization/using-rbac#creating-cluster-admin_using-rbac[Creating a cluster admin]. 
 
-* You have launched the Jupyter application, as described in link:{odhdocshome}/getting-started-with-open-data-hub/#starting-a-jupyter-notebook-server_get-started[Starting a Jupyter notebook server].
+* You have launched the Jupyter application, as described in link:{odhdocshome}/working-with-connected-applications/#starting-a-jupyter-notebook-server_connected-apps[Starting a Jupyter notebook server].
 endif::[]
 
 ifdef::self-managed[]
 * You are part of the {openshift-platform} administrator group. For more information, see link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}/installing-and-deploying-openshift-ai_install#adding-administrative-users-for-{openshift-platform-url}_install[Adding administrative users for {openshift-platform}].
 
-* You have launched the Jupyter application, as described in link:{rhoaidocshome}{default-format-url}/getting_started_with_{url-productname-long}/creating-a-project-workbench_get-started#starting-a-jupyter-notebook-server_get-started[Starting a Jupyter notebook server].
+* You have launched the Jupyter application, as described in link:{rhoaidocshome}{default-format-url}/working-with-connected-applications_connected-apps/starting-a-jupyter-notebook-server_connected-apps[Starting a Jupyter notebook server].
 endif::[]
 
 ifdef::cloud-service[]
 * You are part of the OpenShift Dedicated or Red Hat OpenShift Service on AWS (ROSA) administrator group. For more information, see link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}/installing-and-deploying-openshift-ai_install#adding-administrative-users-in-openshift_install[Adding administrative users].
 
-* You have launched the Jupyter application, as described in link:{rhoaidocshome}{default-format-url}/getting_started_with_{url-productname-long}/creating-a-project-workbench_get-started#starting-a-jupyter-notebook-server_get-started[Starting a Jupyter notebook server].
+* You have launched the Jupyter application, as described in link:{rhoaidocshome}{default-format-url}/working-with-connected-applications_connected-apps/starting-a-jupyter-notebook-server_connected-apps[Starting a Jupyter notebook server].
 endif::[]
 
 * The notebook server that you want to access is running.

modules/creating-a-data-science-project.adoc

@@ -4,16 +4,15 @@
 = Creating a data science project
 
 [role='_abstract']
-To start your data science work, create a data science project. Creating a project helps you organize your work in one place. You can also enhance your data science project by adding the following functionality:
+To implement a data science workflow, you must create a project. In OpenShift, a project is a Kubernetes namespace with additional annotations, and is the main way that you can manage user access to resources. A project organizes your data science work in one place and also allows you to collaborate with other developers and data scientists in your organization.
+
+Within a project, you can add the following functionality: 
+
+* Data connections so that you can access data without having to hardcode information like endpoints or credentials.
+* Workbenches for working with and processing data, and for developing models.
+* Deployed models so that you can test them and then integrate them into intelligent applications. Deploying a model makes it available as a service that you can access by using an API. 
+* Pipelines for automating your ML workflow.
 
-* Workbenches
-* Storage for your project's cluster
-* Data connections
-* Data science pipelines
-* Model servers
-ifdef::upstream[]
-* Bias monitoring for your models
-endif::[]
 
 .Prerequisites
 * You have logged in to {productname-long}.
@@ -25,21 +24,20 @@ ifdef::upstream[]
 endif::[]
 
 .Procedure
-. From the {productname-short} dashboard, click *Data Science Projects*.
-+
-The *Data Science Projects* page opens.
+. From the {productname-short} dashboard, select *Data Science Projects*.
+
 . Click *Create data science project*.
+
+. In the *Create a data science project* dialog, enter a display *Name* for your project.
+
+. Optional: Edit the *Resource name* for your data science project. The resource name must consist of lowercase alphanumeric characters, '-', and must start and end with an alphanumeric character.
 +
-The *Create a data science project* dialog opens.
-. Enter a *name* for your data science project.
-. Optional: Edit the *resource name* for your data science project. The resource name must consist of lowercase alphanumeric characters, '-', and must start and end with an alphanumeric character.
+*Note:* After you create a project, you can change the project display name but you cannot change the resource name.
+
 . Enter a *description* for your data science project.
 . Click *Create*.
-+
-A project details page opens. From this page, you can create workbenches, add cluster storage and data connections, import pipelines, and deploy models.
+
 
 .Verification
-* The project that you created is displayed on the *Data Science Projects* page.
 
-//[role='_additional-resources']
-//.Additional resources//
+* A project details page opens. From this page, you can add data connections, create workbenches, configure pipelines, and deploy models.