ENG-11839: Updates the download instructions for the demo notebooks

assemblies/running-distributed-workloads.adoc

@@ -8,8 +8,11 @@ ifdef::context[:parent-context: {context}]
 [role='_abstract']
 
 In {productname-short}, you can run a distributed workload from a notebook or from a pipeline.
-You can also run distributed workloads in a disconnected environment if you have access to all of the required software.
 
+You can run distributed workloads in a disconnected environment if you can access all of the required software from that environment.
+For example, you must be able to access a Ray cluster image, and the data sets and Python dependencies used by the workload, from the disconnected environment.
+
+include::modules/downloading-the-demo-notebooks-from-the-codeflare-sdk.adoc[leveloffset=+1]
 include::modules/running-distributed-data-science-workloads-from-notebooks.adoc[leveloffset=+1]
 include::modules/running-distributed-data-science-workloads-from-ds-pipelines.adoc[leveloffset=+1]
 ifdef::self-managed[]

modules/downloading-the-demo-notebooks-from-the-codeflare-sdk.adoc

@@ -0,0 +1,80 @@
+:_module-type: PROCEDURE
+
+[id="downloading-the-demo-notebooks-from-the-codeflare-sdk_{context}"]
+= Downloading the demo notebooks from the CodeFlare SDK
+
+[role='_abstract']
+If you want to run distributed workloads from notebooks, the demo notebooks from the CodeFlare SDK provide guidelines on how to use the CodeFlare stack in your own notebooks.
+
+If you do not want to run distributed workloads from notebooks, you can skip this section.
+
+.Prerequisites
+ifndef::upstream[]
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+endif::[]
+ifdef::upstream[]
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+endif::[]
+
+ifndef::upstream[]
+* You can access a data science project that contains a workbench, and the workbench is running a default notebook image that contains the CodeFlare SDK, for example, the *Standard Data Science* notebook. 
+For information about projects and workbenches, see link:{rhoaidocshome}{default-format-url}/working_on_data_science_projects[Working on data science projects].
+endif::[]
+ifdef::upstream[]
+* You can access a data science project that contains a workbench, and the workbench is running a default notebook image that contains the CodeFlare SDK, for example, the *Standard Data Science* notebook. 
+For information about projects and workbenches, see link:{odhdocshome}/working-on-data-science-projects[Working on data science projects].
+endif::[]
+
+* You have Admin access for the data science project.
+** If you created the project, you automatically have Admin access. 
+** If you did not create the project, your cluster administrator must give you Admin access.
+
+* You have logged in to {productname-long}.
+* You have launched your notebook server and logged in to your notebook editor.
+The examples in this procedure refer to the JupyterLab integrated development environment (IDE).
+
+.Procedure
+. In the JupyterLab interface, click *File > New > Notebook*, and then click *Select*. 
++
+A new notebook is created in a `.ipynb` file.
+. Add the following code to a cell in the new notebook:
++
+.Code to download the demo notebooks
+[source,bash]
+----
+from codeflare_sdk import copy_demo_nbs
+copy_demo_nbs()
+----
+
+. Select the cell, and click *Run > Run selected cell*.
++
+After a few seconds, the `copy_demo_nbs()` function copies the demo notebooks that are packaged with the currently installed version of the CodeFlare SDK, and clones them into the `demo-notebooks` folder.
+
+. In the left navigation pane, right-click the new notebook and click *Delete*.
+. Click *Delete* to confirm.
+
+
+.Verification
+Locate the downloaded demo notebooks in the JupyterLab interface, as follows:
+
+. In the left navigation pane, double-click *demo-notebooks*.
+. Double-click *additional-demos* and verify that the folder contains several demo notebooks.
+. Click *demo-notebooks*.
+. Double-click *guided-demos* and verify that the folder contains several demo notebooks. 
+
+ifndef::upstream[]
+You can run these demo notebooks as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/running-distributed-workloads_distributed-workloads#running-distributed-data-science-workloads-from-notebooks_distributed-workloads[Running distributed data science workloads from notebooks].
+endif::[]
+ifdef::upstream[]
+You can run these demo notebooks as described in link:{odhdocshome}/working_with_distributed_workloads/#running-distributed-data-science-workloads-from-notebooks_distributed-workloads[Running distributed data science workloads from notebooks].
+endif::[]
+
+
+////
+[role='_additional-resources']
+.Additional resources
+<Do we want to link to additional resources?>
+
+
+* link:https://url[link text]
+////

modules/running-distributed-data-science-workloads-from-ds-pipelines.adoc

@@ -4,14 +4,14 @@
 = Running distributed data science workloads from data science pipelines
 
 [role='_abstract']
-To run a distributed data science workload from a data science pipeline, you must first update the pipeline to include a link to your Ray cluster image.
+To run a distributed workload from a pipeline, you must first update the pipeline to include a link to your Ray cluster image.
 
 .Prerequisites
 ifndef::upstream[]
-* You have access to a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
 endif::[]
 ifdef::upstream[]
-* You have access to a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
 endif::[]
 
 ifndef::upstream[]
@@ -48,21 +48,28 @@ If your cluster administrator does not define a default local queue, you must sp
 ====
 endif::[]
 
-
-* You have access to S3-compatible object storage.
-* You have logged in to {productname-long}.
+* You can access the following software from your data science cluster:
+** A Ray cluster image that is compatible with your hardware architecture
+** The data sets and models to be used by the workload
+** The Python dependencies for the workload, either in a Ray image or in your own Python Package Index (PyPI) server
 
 ifndef::upstream[]
-* You have created a data science project that contains a workbench, and the workbench is running a default notebook image that contains the CodeFlare SDK, for example, the *Standard Data Science* notebook. For information about how to create a project, see link:{rhoaidocshome}{default-format-url}/working_on_data_science_projects/using-data-science-projects_projects#creating-a-data-science-project_projects[Creating a data science project].
+* You can access a data science project that contains a workbench, and the workbench is running a default notebook image that contains the CodeFlare SDK, for example, the *Standard Data Science* notebook. 
+For information about projects and workbenches, see link:{rhoaidocshome}{default-format-url}/working_on_data_science_projects[Working on data science projects].
 endif::[]
 ifdef::upstream[]
-* You have created a data science project that contains a workbench, and the workbench is running a default notebook image that contains the CodeFlare SDK, for example, the *Standard Data Science* notebook. For information about how to create a project, see link:{odhdocshome}/working-on-data-science-projects/#creating-a-data-science-project_projects[Creating a data science project].
+* You can access a data science project that contains a workbench, and the workbench is running a default notebook image that contains the CodeFlare SDK, for example, the *Standard Data Science* notebook. 
+For information about projects and workbenches, see link:{odhdocshome}/working-on-data-science-projects[Working on data science projects].
 endif::[]
 
 * You have Admin access for the data science project.
 ** If you created the project, you automatically have Admin access. 
 ** If you did not create the project, your cluster administrator must give you Admin access.
 
+* You have access to S3-compatible object storage.
+* You have logged in to {productname-long}.
+
+
 .Procedure
 ifndef::upstream[]
 . Create a data connection to connect the object storage to your data science project, as described in link:{rhoaidocshome}{default-format-url}/working_on_data_science_projects/using-data-connections_projects#adding-a-data-connection-to-your-data-science-project_projects[Adding a data connection to your data science project].
@@ -178,7 +185,9 @@ if __name__ == '__main__': <14>
 If no accelerators are required, set the value to 0 or omit the line. 
 Note: To specify the requested accelerators for the Ray cluster, use the `worker_extended_resource_requests` parameter instead of the deprecated `num_gpus` parameter.
 For more details, see the link:https://github.com/project-codeflare/codeflare-sdk/blob/v0.18.0/src/codeflare_sdk/cluster/config.py#L43-L73[CodeFlare SDK documentation].
-<6> Specifies the location of the Ray cluster image. If you are running this code in a disconnected environment, replace the default value with the location for your environment.
+<6> Specifies the location of the Ray cluster image. 
+The default Ray image is an AMD64 image, which might not work on other architectures.
+If you are running this code in a disconnected environment, replace the default value with the location for your environment. 
 <7> Specifies the local queue to which the Ray cluster will be submitted. If a default local queue is configured, you can omit this line.
 <8> Creates a Ray cluster by using the specified image and configuration.
 <9> Waits until the Ray cluster is ready before proceeding.