RedHatQuickCourses · kknoxrht · Jun 26, 2024 · Jun 26, 2024
diff --git a/modules/ROOT/pages/index.adoc b/modules/ROOT/pages/index.adoc
@@ -8,7 +8,7 @@ Welcome to this quick course on _Serving an LLM using OpenShift AI_.
 
 This program was designed to guide you through the process of installing an OpenShift AI Platform using the OpenShift Container Platform Web Console UI. We get hands-on experience in each component needed to enable a RHOAI Platform using an Openshift Container Platform Cluster. 
 
-Once we have an operational OpenShift AI Platform, we will login and begin the configuration of: Model Runtimes, Data Science Projects, Data connections, & finally use a jupyter notebook to infer the answers to easy questions. 
+Once we have an operational OpenShift AI Platform, we will login and begin the configuration of: model runtimes, data science projects, data connections, & finally use a jupyter notebook to infer the answers to easy questions. 
 
 There will be some challenges along the way, all designed to teach us about a component, or give us the knowledge needed to utilize OpenShift AI and host a Large Language Model. 
 
@@ -39,7 +39,7 @@ We will use the https://demo.redhat.com/catalog?item=babylon-catalog-prod%2Fopen
 [TIP]
 If you are planning on starting this course now, go ahead & launch the workshop. It takes <10 minutes to provision it, which is just enough time to finish the introduction section. 
 
-video::openshiftai_demo.mp4[width=640]
+// video::openshiftai_demo.mp4[width=640]
 
 When ordering this catalog item in RHDP:
 
@@ -53,7 +53,7 @@ When ordering this catalog item in RHDP:
 
   . Click order
 
-For Red Hat partners who do not have access to RHDP, provision an environment using the Red Hat Hybrid Cloud Console. Unfortunately, the labs will NOT work on the trial sandbox environment. You need to provision an OpenShift AI cluster on-premises, or in the supported cloud environments by following the product documentation at https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/2.9/html/installing_and_uninstalling_openshift_ai_self-managed/index[Product Documentation for Red Hat OpenShift AI 2024].
+For Red Hat partners who do not have access to RHDP, provision an environment using the Red Hat Hybrid Cloud Console. Unfortunately, the labs will NOT work on the trial sandbox environment. You need to provision an OpenShift AI cluster on-premises, or in the supported cloud environments by following the product documentation at https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.10/html/installing_and_uninstalling_openshift_ai_self-managed/index[Product Documentation for installing Red Hat OpenShift AI 2.10].
 
 == Prerequisites
 
@@ -77,4 +77,4 @@ The overall objectives of this course include:
 
  * Import (from git repositories), interact with LLM model via Jupyter Notebooks
 
- * Experiment with the Mistral LLM
+ * Experiment with the Mistral LLM and Llama3 large language models
diff --git a/modules/chapter2/images/openshift_ai_operator_search.png b/modules/chapter2/images/openshift_ai_operator_search.png
diff --git a/modules/chapter2/pages/index.adoc b/modules/chapter2/pages/index.adoc
@@ -7,7 +7,7 @@ OpenShift AI is supported in two configurations:
 For information about OpenShift AI on a Red Hat managed environment, see https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_cloud_service/1[Product Documentation for Red Hat OpenShift AI Cloud Service].
 
 * Self-managed software that you can install on-premise or on the public cloud in a self-managed environment, such as *OpenShift Container Platform*.
-For information about OpenShift AI as self-managed software on your OpenShift cluster in a connected or a disconnected environment, see https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/2.8[Product Documentation for Red Hat OpenShift AI Self-Managed 2.8].
+For information about OpenShift AI as self-managed software on your OpenShift cluster in a connected or a disconnected environment, see https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.10[Product Documentation for Red Hat OpenShift AI Self-Managed 2.10].
 
 In this course we cover installation of *Red Hat OpenShift AI self-managed* using the OpenShift Web Console.
 
@@ -22,25 +22,17 @@ The product name has been recently changed to *Red{nbsp}Hat OpenShift AI (RHOAI)
 In addition to the *Red{nbsp}Hat OpenShift AI* Operator there are additional operators that you may need to install depending on which features and components of *Red{nbsp}Hat OpenShift AI* you want to utilize.
 
 
-[NOTE]
-====
-To support the KServe component, which is used by the single-model serving platform to serve large models, install the Operators for Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh. 
-====
-
-https://docs.openshift.com/container-platform/latest/hardware_enablement/psap-node-feature-discovery-operator.html[OpenShift Serveless Operator]::
-// Is this the correct link for OpenShift Serveless Operator?
-The *OpenShift Serveless Operator* is a prerequisite for the *Single Model Serving Platform*.
+https://www.redhat.com/en/technologies/cloud-computing/openshift/serverless[Red{nbsp}Hat OpenShift Serverless Operator]::
+The *Red Hat OpenShift Serverless operator* provides a collection of APIs that enables containers, microservices and functions to run "serverless". The *Red{nbsp}Hat OpenShift Serverless Operator* is required if you want to install the Single-model serving platform component.
 
-https://docs.openshift.com/container-platform/latest/hardware_enablement/psap-node-feature-discovery-operator.html[OpenShift Service Mesh Operator]::
-// Is this the correct link for OpenShift Service Mesh Operator?
-The *OpenShift Service Mesh Operator* is a prerequisite for the *Single Model Serving Platform*.
+https://catalog.redhat.com/software/container-stacks/detail/5ec53e8c110f56bd24f2ddc4[Red{nbsp}Hat OpenShift Service Mesh Operator]::
+*Red Hat OpenShift Service Mesh operator* provides an easy way to create a network of deployed services that provides discovery, load balancing, service-to-service authentication, failure recovery, metrics, and monitoring. The *Red{nbsp}Hat OpenShift Serverless Operator* is required if you want to install the Single-model serving platform component.
 
-https://www.redhat.com/en/technologies/cloud-computing/openshift/pipelines[Red{nbsp}Hat OpenShift Pipelines Operator]::
-The *Red{nbsp}Hat OpenShift Pipelines Operator* is a prerequisite for the *Single Model Serving Platform*.  
+https://developers.redhat.com/articles/2021/06/18/authorino-making-open-source-cloud-native-api-security-simple-and-flexible[Red{nbsp}Hat Authorino (technical preview) Operator]::
+*Red Hat Authorino* is an open source, Kubernetes-native external authorization service to protect APIs. The *Red{nbsp}Hat Authorino Operator* is required to support enforcing authentication policies in Red Hat OpenShift AI.
 
 
-
-[NOTE]
+[INFO]
 ====
 The following Operators are required to support the use of Nvidia GPUs (accelerators) with OpenShift AI:
 ====

diff --git a/modules/chapter2/pages/section1.adoc b/modules/chapter2/pages/section1.adoc
@@ -2,7 +2,7 @@
 
 *Red{nbsp}Hat OpenShift AI* is available as an operator via the OpenShift Operator Hub.  You will install the *Red{nbsp}Hat OpenShift AI operator* and dependencies using the OpenShift web console in this section.
 
-video::openshiftai_operator.mp4[width=640]
+// video::openshiftai_operator.mp4[width=640]
 
 == Lab: Installation of Red{nbsp}Hat OpenShift AI
 
@@ -19,13 +19,11 @@ This exercise uses the Red Hat Demo Platform; specifically the OpenShift Contain
 [*] You do not have to wait for the previous Operator to complete before installing the next. For this lab you can skip the installation of the optional operators as there is no GPU.
 // Should this be a note?
 
-    * Web Terminal 
-
     * Red Hat OpenShift Serverless 
 
     * Red Hat OpenShift Service Mesh
 
-    * Red Hat OpenShift Pipelines 
+    * Red Hat Authorino technical preview
 
     * GPU Support
 
@@ -41,7 +39,7 @@ This exercise uses the Red Hat Demo Platform; specifically the OpenShift Contain
 
 image::openshiftai_operator.png[width=640]
 
-. Click on the `Red{nbsp}Hat OpenShift AI` operator. In the pop up window that opens, ensure you select the latest version in the *fast* channel. Any version greater than 2.91 and click on **Install** to open the operator's installation view.
+. Click on the `Red{nbsp}Hat OpenShift AI` operator. In the pop up window that opens, ensure you select the latest version in the *stable* channel. Any version greater than 2.10 and click on **Install** to open the operator's installation view.
 +
 
 . In the `Install Operator` page, leave all of the options as default and click on the *Install* button to start the installation.

diff --git a/modules/chapter2/pages/section3.adoc b/modules/chapter2/pages/section3.adoc
@@ -42,9 +42,10 @@ Congratulations, you have successfully completed the installation of OpenShift A
 
 
   * We installed the required OpenShift AI Operators
-  ** Serverless, ServiceMesh, & Pipelines Operators
+  ** Red Hat OpenShift Serverless 
+  ** Red Hat OpenShift ServiceMesh
+  ** Red Hat Authorino (technical preview)
   ** OpenShift AI Operator
-  ** Web Terminal Operator
 
 Additionally, we took this installation a step further by sharing TLS certificates from the OpenShift Cluster with OpenShift AI. 
 

diff --git a/modules/chapter3/pages/section1.adoc b/modules/chapter3/pages/section1.adoc
@@ -4,13 +4,17 @@ video::llm_dsp_v3.mp4[width=640]
 
 == Model Serving Runtimes
 
-A model-serving runtime provides integration with a specified model server and the model frameworks that it supports. By default, Red Hat OpenShift AI includes the following Model RunTimes:
+A model-serving runtime provides integration with a specified model server and the model frameworks that it supports. By default, Red Hat OpenShift AI includes the following model serving runTimes:
 
- * OpenVINO Model Server runtime.
+ Multi-model
+ * OpenVINO Model Server - Multi-model
+ Single-model
+ * OpenVINO Model Server
  * Caikit TGIS for KServe
- * TGIS Standalone for KServe 
+ * TGIS Standalone for KServe
+ * vLLM For KServe 
 
-However, if these runtime do not meet your needs (if they don't support a particular model framework, for example), you might want to add your own custom runtimes.
+However, if these runtimes do not meet your needs (if they don't support a particular model framework, for example), you might want to add your own custom runtimes.
 
 As an administrator, you can use the OpenShift AI interface to add and enable custom model-serving runtimes. You can then choose from your enabled runtimes when you create a new model server.
 
@@ -49,7 +53,7 @@ spec:
   builtInAdapter:
     modelLoadingTimeoutMillis: 90000
   containers:
-    - image: quay.io/rh-aiservices-bu/ollama-ubi9:0.1.30
+    - image: quay.io/rh-aiservices-bu/ollama-ubi9:0.1.45
       env:
         - name: OLLAMA_MODELS
           value: /.ollama/models