ch02s01: Data Science Projects (#4)

antora-playbook.yml

@@ -8,6 +8,7 @@ content:
 
 asciidoc:
   attributes:
+    experimental: true
     page-pagination: true
 
 ui:

modules/chapter1/pages/section3.adoc

@@ -1,7 +1,7 @@
 :experimental:
 = Demo
 
-In this exercise, you will use RHODS to train and evaluate a simple machine learning model, by using JuypterLab and other common ML libraries.
+In this exercise, you will use RHODS to train and evaluate a simple machine learning model, by using JupyterLab and other common ML libraries.
 
 
 1. *Open the RHODS dashboard.*
@@ -18,7 +18,7 @@ c. If prompted, log in with your Red{nbsp}Hat OpenShift credentials.
 
 2. *Configure the workbench of your data science project.*
 +
-A RHODS workbench is a containerized application that includes commonly used data science tools and libraries, such as JupyterLab, Tensorflow, and PyTorch.
+A RHODS workbench is a containerized application that includes commonly used data science tools and libraries, such as JupyterLab, TensorFlow, and PyTorch.
 RHODS provides you with a collection of workbench container images, each one preconfigured and tailored to a specific data science use case.
 
 a. Click btn:[Data Science Projects] in the left sidebar.
@@ -67,7 +67,7 @@ b. If prompted, log in with your Red{nbsp}Hat OpenShift credentials.
 
 c. Click btn:[Allow selected permissions] to grant the workbench access to your data science project.
 
-d. Verify that the JuyperLab interface opens in a new browser tab.
+d. Verify that the JupyterLab interface opens in a new browser tab.
 
 e. Click the btn:[Git] icon in the left sidebar.
 
@@ -94,7 +94,7 @@ Keep pressing btn:[Shift+Enter] until you reach the bottom.
 
 a. In the data science project dashboard, create a workbench with the default image for PyTorch.
 
-b. In the new worbench, open JuypterLab and clone the https://github.com/RedHatQuickCourses/rhods-intro.git repository as you did in the previous step.
+b. In the new workbench, open JupyterLab and clone the https://github.com/RedHatQuickCourses/rhods-intro.git repository as you did in the previous step.
 
-c. Open the `rhods-intro/intro-text-generation/notebook.ipynb` notebook in Jupyter and execute the steps.
+c. Open the `rhods-intro/intro-text-generation/notebook.ipynb` notebook in JupyterLab and execute the steps.
 The instructions are embedded in the notebook.

modules/chapter1/pages/section4.adoc

@@ -7,7 +7,7 @@ In this chapter, you learned:
 * RHODS helps teams bridge gap between data science and engineering, by operationalizing the lifecycle of AI projects.
 * RHODS is based on the upstream open source OpenDataHub project.
 * RHODS provides users with ready-to-use environments that include stable versions of the most common AI/ML tools.
-* Users can seamlesly run training pipelines, deploy, and monitor their models in production environments.
+* Users can seamlessly run training pipelines, deploy, and monitor their models in production environments.
 
 == Next Steps
 

modules/chapter2/pages/index.adoc

@@ -1,3 +1,9 @@
-= Chapter 2
+= Working with Data Science Projects
 
-This is the home page of _Chapter 2_ in the *hello* quick course....
+This chapter introduces you to the initial steps and concepts required to start your projects in RHODS.
+You will learn how to use RHODS to organize your work in data science projects.
+You will also learn how to create working environments and connect those environments to storage providers.
+
+Chapter goals:
+
+* Organize code and configuration using data science projects, workbenches, and data connections

modules/chapter2/pages/section1.adoc

@@ -1,3 +1,88 @@
-= Section 1
+= Data Science Projects
 
-This is _Section 1_ of _Chapter 2_ in the *hello* quick course....
+In RHODS, a _data science project_ is the preferred way to organize resources when working on an AI/ML application.
+Similarly to how you use projects in OpenShift for other workloads, you can use and should use data science projects to organize the different elements that you need for your AI applications, such as workbenches, model servers, or persistent storage.
+
+You can create and manage data science projects by using the RHODS dashboard.
+
+Internally, a data science project is an OpenShift project that contains specific labels to identify the project as a RHODS-specific project,
+for example:
+
+[source,yaml,subs="+quotes"]
+----
+kind: Project
+apiVersion: project.openshift.io/v1
+metadata:
+  name: my-ai-project
+  labels:
+    kubernetes.io/metadata.name: my-ai-project
+    *modelmesh-enabled: 'true'*
+    *opendatahub.io/dashboard: 'true'*
+    ...
+----
+
+[IMPORTANT]
+====
+Regular OpenShift projects are not shown in the RHODS dashboard.
+
+If you cannot use the RHODS dashboard, for example because you are automating the creation of your projects, then make sure that the `opendatahub.io/dashboard: 'true'` label is included in the project definition.
+This label identifies the project as a data science project.
+
+Additionally, if you wish to use model serving in your project, then you must add the `modelmesh-enabled: 'true'` label.
+====
+
+
+
+== Exercise
+
+=== Creating Data Science Projects
+
+First, open the RHODS dashboard.
+You might need to enter your credentials to access the dashboard.
+
+To create a data science project, click btn:[Data Science Projects] in the left navigation pane.
+
+image::rhods-side-menu.png[width=50%]
+
+In the btn:[Data science projects] page, click btn:[Create data science project].
+
+In the modal window that opens, enter the name of the project and click btn:[Create].
+You can optionally add a description and modify the OpenShift resource name for the project.
+
+image::create-ds-project.png[]
+
+After you create the project, RHODS opens the project dashboard page.
+
+image::ds-project-dashboard.png[]
+
+From this page you can manage the project components and the permissions.
+
+
+=== Configure Access
+
+By default, only the project owner and privileged users can access your projects in RHODS.
+However, you can add permissions for specific users and groups.
+
+To configure permissions, from a project dashboard page, click the btn:[Permissions] tab.
+Then, add users and groups, and specify their permissions.
+
+image::ds-project-permissions.png[]
+
+// TODO: Link to the admin course (user and groups creation)
+
+=== Deleting Data Science Projects
+
+To delete a data science project, navigate to the btn:[Data science projects] page.
+Then, locate the project to be deleted and click its btn:[⋮] button, then click btn:[Delete project].
+Finally, type the project name to confirm the deletion.
+
+image::ds-project-delete.png[]
+
+
+// Adding and deleting cluster storage to the project
+// [Jaime R] we should cover this after the creation of a workbench
+
+// == TODO: Disabled self-provisioning
+// [Trevor] Include details about how to manage a DS project when Self Provisioning is disabled on a cluster (e.g. annotations on namespaces).
+// [Jaime R] This might be more suitable for the admin-oriented quick course.
+// TODO: link to the admin course section that teaches this