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b/modules/chapter1/pages/dependencies-install-web-console.adoc @@ -6,8 +6,15 @@ It is generally recommended to install any dependent operators prior to installi // This section given below is the same as in the previous chapter. Is the whole section with explanation required here again? -https://www.redhat.com/en/technologies/cloud-computing/openshift/pipelines[Red{nbsp}Hat OpenShift Pipelines Operator]:: -The *Red Hat OpenShift Pipelines Operator* is required if you want to install the *Data Science Pipelines* component. +https://www.redhat.com/en/technologies/cloud-computing/openshift/serverless[Red{nbsp}Hat OpenShift Serverless Operator]:: +The *Red{nbsp}Hat OpenShift Serverless Operator* is required if you want to install the *single-model serving platform component*. + +https://catalog.redhat.com/software/container-stacks/detail/5ec53e8c110f56bd24f2ddc4[Red{nbsp}Hat OpenShift Service Mesh Operator]:: +The *Red{nbsp}Hat OpenShift Serverless Operator* is required if you want to install the *single-model serving platform component*. + +https://developers.redhat.com/articles/2021/06/18/authorino-making-open-source-cloud-native-api-security-simple-and-flexible[Red{nbsp}Hat OpenShift Authorino (technical preview) Operator]:: +The *Red{nbsp}Hat Authorino Operator* is required to support enforcing authentication policies in *Red Hat OpenShift AI*. + https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/index.html[NVIDIA GPU Operator]:: The *NVIDIA GPU Operator* is required for GPU support in *Red Hat OpenShift AI*. https://docs.openshift.com/container-platform/latest/hardware_enablement/psap-node-feature-discovery-operator.html[Node Feature Discovery Operator]:: @@ -15,39 +22,95 @@ The *Node Feature Discovery Operator* is a prerequisite for the *NVIDIA GPU Oper This section will discuss the process for installing the dependent operators using the OpenShift Web Console. -== Installation of Data Science Pipelines Dependencies +== Installation of Red Hat OpenShift Serverless Dependencies + +The following section discusses installing the *Red{nbsp}Hat OpenShift Serverless* operator. + +=== Lab: Installation of the *Red{nbsp}Hat OpenShift Serverless* operator + +1. Login to Red{nbsp}Hat OpenShift using a user which has the _cluster-admin_ role assigned. + +2. Navigate to **Operators** -> **OperatorHub** and search for *Red{nbsp}Hat OpenShift Serverless* ++ +image::serverless_operator_search.png[width=800] + +3. Click on the *Red{nbsp}Hat OpenShift Serverless* operator. In the pop up window, select the *stable* channel and the most recent version of the serverless operator. Click on **Install** to open the operator's installation view. ++ +image::serverless_operator_install1.png[width=600] + +4. In the `Install Operator` page, select the default values for all the fields and click *Install*. ++ +image::serverless_operator_install2.png[width=800] + +5. A window showing the installation progress will pop up. ++ +image::serverless_operator_install3.png[width=800] + +6. When the installation finishes the operator is ready to be used by *Red{nbsp}Hat OpenShift AI*. ++ +image::serverless_operator_install4.png[width=800] + +*Red{nbsp}Hat OpenShift Serverless* is now successfully installed. + +== Installation of Red Hat OpenShift Service Mesh + +The following section discusses installing the *Red{nbsp}Hat OpenShift Service Mesh* operator. + +=== Lab: Installation of the *Red{nbsp}Hat OpenShift Service Mesh* operator + +1. Login to Red{nbsp}Hat OpenShift using a user which has the _cluster-admin_ role assigned. + +2. Navigate to **Operators** -> **OperatorHub** and search for *Red{nbsp}Hat OpenShift Service Mesh* ++ +image::servicemesh_operator_search.png[width=800] + +3. Click on the *Red{nbsp}Hat OpenShift Service Mesh* operator. In the pop up window, select the *stable* channel and the most recent version of the server mesh operator. Click on **Install** to open the operator's installation view. ++ +image::servicemesh_operator_install1.png[width=600] + +4. In the `Install Operator` page, select the default values for all the fields and click *Install*. ++ +image::servicemesh_operator_install2.png[width=800] + +5. A window showing the installation progress will pop up. ++ +image::servicemesh_operator_install3.png[width=800] + +6. When the installation finishes the operator is ready to be used by *Red{nbsp}Hat OpenShift AI*. ++ +image::servicemesh_operator_install4.png[width=800] -The Data Science Pipelines component utilizes *Red{nbsp}Hat OpenShift Pipelines* as an execution engine for all pipeline runs, and is required to be installed to take advantage of the Data Science Pipelines component. +*Red{nbsp}Hat OpenShift Service Mesh* is now successfully installed. -The following section discusses installing the *Red{nbsp}Hat OpenShift Pipelines* operator. +== Installation of Red Hat Authorino -=== Lab: Installation of the *Red{nbsp}Hat OpenShift Pipelines* operator +=== Lab: Installation of the *Red{nbsp}Hat Authorino* operator 1. Login to Red{nbsp}Hat OpenShift using a user which has the _cluster-admin_ role assigned. -2. Navigate to **Operators** -> **OperatorHub** and search for *Red{nbsp}Hat OpenShift Pipelines* +2. Navigate to **Operators** -> **OperatorHub** and search for *Red{nbsp}Hat Authorino* + -image::pipeline_search.png[width=800] +image::authorino_operator_search.png[width=800] -3. Click on the *Red{nbsp}Hat OpenShift Pipelines* operator. In the pop up window, select the *latest* channel and the most recent version of the pipelines operator. Click on **Install** to open the operator's installation view. +3. Click on the *Red{nbsp}Hat Authorino* operator. In the pop up window, select the *stable* channel and the most recent version of the serverless operator. Click on **Install** to open the operator's installation view. + -image::pipeline_install1.png[width=800] +image::authorino_operator_install1.png[width=600] 4. In the `Install Operator` page, select the default values for all the fields and click *Install*. + -image::pipeline_install2.png[width=800] +image::authorino_operator_install2.png[width=800] 5. A window showing the installation progress will pop up. + -image::pipeline_install3.png[width=800] +image::authorino_operator_install3.png[width=800] 6. When the installation finishes the operator is ready to be used by *Red{nbsp}Hat OpenShift AI*. + -image::pipeline_install4.png[width=800] +image::authorino_operator_install4.png[width=800] -*Red{nbsp}Hat OpenShift Pipelines* is now successfully installed. +*Red{nbsp}Hat Authorino* is now successfully installed. -TIP: For assistance in installing OpenShift Pipelines from YAML or via ArgoCD, refer to examples found in the https://github.com/redhat-cop/gitops-catalog/tree/main/openshift-pipelines-operator[redhat-cop/gitops-catalog/openshift-pipelines-operator] GitHub repo. +The following section discusses installing the *Red{nbsp}Hat - Authorino* operator. == Lab: Installation of GPU Dependencies @@ -67,19 +130,19 @@ TIP: To view the list of GPU models supported by the *NVIDIA GPU Operator* refer 2. Navigate to **Operators** -> **OperatorHub** and search for *Node Feature Discovery* + -image::nfd_search.png[width=800] +image::node_feature_discovery_search.png[width=800] 3. Two options for the *Node Feature Discovery* operator will be available. Click on the one with *Red Hat* in the top right hand corner, and in the pop up window click on **Install** to open the operator's installation view. + IMPORTANT: Make sure you select *Node Feature Discovery* from *Red{nbsp}Hat* NOT the Community version. + -image::nfd_install1.png[width=800] +image::node_feature_discovery_install1.png[width=800] 4. In the `Install Operator` page, select the option to *Enable Operator recommended cluster monitoring on this Namespace*, and keep all the rest of the parameters at their default values. + NOTE: Some of these options may vary slightly depending on your version of OpenShift. Please refer to the official Node Feature Discovery Documentation for your version of OpenShift for the recommended settings. + -image::nfd_install2.png[width=800] +image::node_feature_discovery_install2.png[width=800] 5. Click the **Install** button at the bottom of the page to proceed with the installation. A window showing the installation progress will pop up. @@ -87,13 +150,13 @@ image::nfd_install2.png[width=800] 7. Click the **Create instance** button for the *NodeFeatureDiscovery* object. + -image::nfd_configure1.png[width=800] +image::node_feature_discovery_instance1.png[width=800] 8. In the `Create NodeFeatureDiscovery` page, leave all fields at their default values, and click the **Create** button. 9. A new set of pods should appear in the **Workloads** -> **Pods** section managed by the *nfd-worker* DaemonSet. Node Feature Discovery will now be able to automatically detect information about the nodes in the cluster and apply labels to those nodes. + -image::nfd_verify.png[width=800] +image::nvidia_gpu_pods.png[width=800] TIP: For assistance in installing the Node Feature Discovery Operator from YAML or via ArgoCD, refer to examples found in the https://github.com/redhat-cop/gitops-catalog/tree/main/nfd[redhat-cop/gitops-catalog/nfd] GitHub repo. @@ -105,15 +168,15 @@ TIP: For assistance in installing the Node Feature Discovery Operator from YAML 2. Navigate to **Operators** -> **OperatorHub** and search for *NVIDIA GPU Operator* + -image::gpu_search.png[width=800] +image::nvidia_operator_search.png[width=800] 3. Click the `NVIDIA GPU Operator` tile. In the pop up window leave all fields at their default values, and click on **Install** to open the operator's installation view. + -image::gpu_install1.png[width=800] +image::nvidia_gpu_operator1.png[width=800] 4. In the `Install Operator` page, keep all the parameters at their default values, and click the **Install** button at the bottom of the page to proceed with the installation. + -image::gpu_install2.png[width=800] +image::nvidia_gpu_operator2.png[width=800] 5. A window showing the installation progress will pop up. Wait while the operator finishes installing. @@ -121,13 +184,13 @@ image::gpu_install2.png[width=800] 7. Click the **Create instance** button for the *ClusterPolicy* object. + -image::gpu_configure1.png[width=800] +image::nvidia_gpu_instance1.png[width=800] 8. In the `Create ClusterPolicy` page, leave all fields at their default values, and click the **Create** button. 9. After the *ClusterPolicy* is created, the *NVIDIA GPU Operator* will update the status *State: ready*. + -image::gpu_verify1.png[width=800] +image::nvidia_gpu_instance2.png[width=800] 10. After the *Red{nbsp}Hat OpenShift AI* operator has been installed and configured, users will be able to see an option for "Number of GPUs" when creating a new workbench. + diff --git a/modules/chapter1/pages/index.adoc b/modules/chapter1/pages/index.adoc index 7ebab26..c890bec 100644 --- a/modules/chapter1/pages/index.adoc +++ b/modules/chapter1/pages/index.adoc @@ -7,6 +7,6 @@ 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 1] * 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]. In this course we cover installation of *Red Hat OpenShift AI self-managed* using the OpenShift Web Console. diff --git a/modules/chapter1/pages/install-general-info.adoc b/modules/chapter1/pages/install-general-info.adoc index e1cf55c..5b5ffe7 100644 --- a/modules/chapter1/pages/install-general-info.adoc +++ b/modules/chapter1/pages/install-general-info.adoc @@ -7,10 +7,16 @@ Red{nbsp}Hat OpenShift AI is available to install as an operator through the *Op The product name has been recently changed to *Red{nbsp}Hat OpenShift AI (RHOAI)* (old name *Red{nbsp}Hat OpenShift Data Science*). In this course, most references to the product use the new name. However, references to some UI elements might still use the previous name. ==== -In addition to the *Red{nbsp}Hat OpenShift AI* Operator there are some other operators that you may need to install depending on which features and components of *Red{nbsp}Hat OpenShift AI* you want to install and use. +In addition to the *Red{nbsp}Hat OpenShift AI* Operator there are some other operators that you may need to install depending on which features and components of *Red{nbsp}Hat OpenShift AI* you want to install and use. -https://www.redhat.com/en/technologies/cloud-computing/openshift/pipelines[Red{nbsp}Hat OpenShift Pipelines Operator]:: -The *Red{nbsp}Hat OpenShift Pipelines Operator* is required if you want to install the *Red{nbsp}Hat OpenShift AI Pipelines* component. +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://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://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. https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/index.html[NVIDIA GPU Operator]:: The *NVIDIA GPU Operator* is required for GPU support in Red Hat OpenShift AI. diff --git a/modules/chapter1/pages/rhods-install-web-console.adoc b/modules/chapter1/pages/rhods-install-web-console.adoc index d721b02..4c4f162 100644 --- a/modules/chapter1/pages/rhods-install-web-console.adoc +++ b/modules/chapter1/pages/rhods-install-web-console.adoc @@ -10,12 +10,12 @@ IMPORTANT: The installation requires a user with the _cluster-admin_ role . Navigate to **Operators** -> **OperatorHub** and search for *OpenShift AI*. + -image::rhods_install1.png[title=Search for OpenShift AI operator,width=800] +image::openshift_ai_operator_search.png[title=Search for OpenShift AI operator,width=800] // The sentence in this image is not captured correctly . 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 and click on **Install** to open the operator's installation view. + -image::rhods_install2.png[title=OpenShift AI Operator Details,width=800] +image::openshift_ai_operator_install2.png[title=OpenShift AI Operator Details,width=800] . In the `Install Operator` page, leave all of the options as default and click on the *Install* button to start the installation. @@ -27,12 +27,12 @@ image::rhods_install2.png[title=OpenShift AI Operator Details,width=800] . After creating the *DataScienceCluster*, a view showing the *DataScienceCluster* details opens. Wait until the status of the cluster reads *Phase: Ready*. This represents the status of the whole cluster. + -image::rhods2-clusters.png[title=DataScienceCluster Instance Ready,width=800] +image::openshift_ai_dsc_cluster2.png[title=DataScienceCluster Instance Ready,width=800] . The operator should be installed and configured now. In the applications window in the right upper corner of the screen the *Red{nbsp}Hat OpenShift AI* dashboard should be available. + -image::rhods_verify1.png[title=RHOAI Dashboard] +image::red_hat_openshift_ai1.png[title=RHOAI Dashboard] . Click the *Red{nbsp}Hat OpenShift AI* button to log in to the *Red{nbsp}Hat OpenShift AI* dashboard. Log in as the *admin* user (With the same password that you used to log in to the OpenShift web console). + @@ -40,11 +40,11 @@ image::rhods_verify2.png[title=Red Hat OpenShift AI Log in,width=800] . You should be able to see the Red Hat OpenShift AI home page. + -image::rhoai-home.png[title=Red Hat OpenShift AI Home Page] +image::openshiftai_dashboard1.png[title=Red Hat OpenShift AI Home Page] + IMPORTANT: It may take a while to start all the service pods hence the login window may not be accessible immediately. If you are getting an error, check the status of the pods in the project *redhat-ods-applications*. Navigate to *Workloads* -> *pods* and select project *redhat-ods-applications*. All pods must be running and be ready. If they are not, wait until they become running and ready. + -image::rhods_verify_pods.png[title=Pods in Running state,width=800] +image::openshiftai_pods.png[title=Pods in Running state,width=800] TIP: For assistance installing the *Red{nbsp}Hat Openshift AI* from YAML or via ArgoCD, refer to examples found in the https://github.com/redhat-cop/gitops-catalog/tree/main/rhods-operator[redhat-cop/gitops-catalog/rhods-operator] GitHub repo. diff --git a/modules/chapter1/pages/uninstalling-rhods.adoc b/modules/chapter1/pages/uninstalling-rhods.adoc index c5a19ed..beb2c4b 100644 --- a/modules/chapter1/pages/uninstalling-rhods.adoc +++ b/modules/chapter1/pages/uninstalling-rhods.adoc @@ -16,7 +16,7 @@ Make sure that you have installed the *Red{nbsp}Hat OpenShift AI* operator using + Navigate to *Operators* -> *Installed Operators* -> *Red Hat OpenShift AI* -> *DataScienceCluster* and delete all the *DSC* resources. + -image::rhods2-delete-dsc.png[title=Delete DSC Resource] +image::uninstall_dsc_delete2.png[title=Delete DSC Resource] + Alternatively, you can delete the *DataScienceCluster* objects from the CLI. + @@ -61,7 +61,7 @@ subscription.operators.coreos.com "rhods-operator" deleted + Navigate to *Administration* -> *Namespaces*, search for the `redhat-ods-operator` and delete it. + -image::rhods2-delete-ope-ns.png[width=800] +image::uninstall_rhods_namespace.png[width=800] + Alternatively you can delete the namespace from the CLI. + @@ -77,7 +77,7 @@ Deleting namespace _rhods-notebooks_ leads to Persistent Volume Claims (PVC) bei + Navigate to *Administration* -> *Namespaces*, filter the namespaces using the label _opendatahub.io/generated-namespace=true_ and delete them. + -image::rhods2-delete-generated-ns.png[width=800] +image::rhods_manual_component-remove.png[width=800] + Alternatively you can delete the namespaces from the CLI. + @@ -92,7 +92,7 @@ namespace "redhat-ods-monitoring" deleted + Navigate to *Home* -> *Projects*, filter namespaces using the label _opendatahub.io/dashboard=true_ and delete them. + -image::rhods2-delete-projects.png[width=800] +image::dsproject_removal.png[width=800] + Alternatively you can delete them from the CLI. + @@ -105,19 +105,19 @@ namespace "my-rhods-project" deleted == Uninstalling the Red{nbsp}Hat OpenShift AI dependencies. If you have installed some dependencies you can remove them as long as they are not used by other deployments. -The following demonstration shows uninstallation of the *Red{nbsp}Hat OpenShift Pipelines* operator. +// The following demonstration shows uninstallation of the *Red{nbsp}Hat Authorino operator. -[#demo-pipelines] -=== Demo: Uninstallation of the *Red{nbsp}Hat OpenShift Pipelines* operator +[#demo-authorino] +=== Demo: Uninstallation of the *Red{nbsp}Hat Authorino* operator . Log in to Red{nbsp}Hat OpenShift web console using a user which has the _cluster-admin_ role assigned. -. Navigate to *Operators* -> *Installed Operators*, choose *All Projects* and click on the three dots on the right side of the *Red{nbsp}Hat OpenShift Pipelines* operator. +. Navigate to *Operators* -> *Installed Operators*, choose *All Projects* and click on the three dots on the right side of the *Red{nbsp}Hat Authorino* operator. + -image::pipelines-uninstall.png[width=800] +image::uninstall_authorino1.png[width=800] + Click on Uninstall operator. . In the pop-up window scroll down, check *Delete all operand instances for this operator* and click on *Uninstall*. + -image::piplines-uninstall-confirm.png[width=800] +image::uninstall_authorino2.png[width=800] diff --git a/modules/chapter1/pages/upgrading-rhods.adoc b/modules/chapter1/pages/upgrading-rhods.adoc index 10a12d3..c09293b 100644 --- a/modules/chapter1/pages/upgrading-rhods.adoc +++ b/modules/chapter1/pages/upgrading-rhods.adoc @@ -33,7 +33,7 @@ Make sure that you have installed the *Red{nbsp}Hat OpenShift AI* operator using . Navigate to *Operators* -> *Installed Operators*, select project *redhat-ods-operator*, click on the *Red{nbsp}Hat OpenShift AI* and choose the *Subscription* tab. Edit the *Update channel* - change it to *alpha* and click *Save*. + -image::rhods2-channel-change.png[width=800] +image::openshiftai_upgrade1.png[width=800] + NOTE: There may not be any updates available at the time when you do this. In which case, your screen will look different in the following steps. diff --git a/modules/chapter2/images/create_dsp_project.png b/modules/chapter2/images/create_dsp_project.png new file mode 100644 index 0000000..4058456 Binary files /dev/null and b/modules/chapter2/images/create_dsp_project.png differ diff --git a/modules/chapter2/images/users_groups_manage1.png b/modules/chapter2/images/users_groups_manage1.png new file mode 100644 index 0000000..0a1f6f3 Binary files /dev/null and b/modules/chapter2/images/users_groups_manage1.png differ diff --git a/modules/chapter2/images/users_groups_manage2.png b/modules/chapter2/images/users_groups_manage2.png new file mode 100644 index 0000000..34796bb Binary files /dev/null and b/modules/chapter2/images/users_groups_manage2.png differ diff --git a/modules/chapter2/pages/users.adoc b/modules/chapter2/pages/users.adoc index 0c5d80d..e3409b0 100644 --- a/modules/chapter2/pages/users.adoc +++ b/modules/chapter2/pages/users.adoc @@ -40,7 +40,7 @@ oc adm groups add-users rhods-admins my-user1 my-user2 The default permissions for the OpenShift AI Dashboard can be updated through the menu:Settings[User management] section. -image::user-management.png[] +image::users_groups_manage2.png[] The admin group can be updated to another group other than the default `rhods-admins` group, such as one that is configured to sync with an external system. Additionally, admin permissions to the Dashboard are always granted to users assigned the `cluster-admin` ClusterRole. @@ -91,7 +91,7 @@ This object can be exported from the cluster and stored in a git repository to e The OpenShift AI Dashboard permits users of the Dashboard to create new Data Science Projects when self-provisioning is enabled on the cluster: -image::create-datascience-project.png[] +image::create_dsp_project.png[] [NOTE] ==== @@ -128,7 +128,7 @@ Users and Groups permissions to a project are managed using Kubernetes Role Base An admin user on the project can add additional users or groups to a project by navigating to a specific project under the Data Science Projects menu, and selecting the Permissions tab. -image::datascience-project-permissions.png[] +image::users_groups_manage1.png[] From the permissions tab, project admin users are able to add additional users or groups and grant them the `edit` or `admin` role on the project. diff --git a/modules/chapter3/images/create_dsp_project.png b/modules/chapter3/images/create_dsp_project.png new file mode 100644 index 0000000..4058456 Binary files /dev/null and b/modules/chapter3/images/create_dsp_project.png differ diff --git a/modules/chapter3/images/custom_image_notebook1.png b/modules/chapter3/images/custom_image_notebook1.png new file mode 100644 index 0000000..c9285ce Binary files /dev/null and b/modules/chapter3/images/custom_image_notebook1.png differ diff --git a/modules/chapter3/images/custom_image_notebook2.png b/modules/chapter3/images/custom_image_notebook2.png new file mode 100644 index 0000000..1454092 Binary files /dev/null and b/modules/chapter3/images/custom_image_notebook2.png differ diff --git a/modules/chapter3/images/custom_image_notebook3.png b/modules/chapter3/images/custom_image_notebook3.png new file mode 100644 index 0000000..9ca5e96 Binary files /dev/null and b/modules/chapter3/images/custom_image_notebook3.png differ diff --git a/modules/chapter3/images/custom_image_workbench.png b/modules/chapter3/images/custom_image_workbench.png new file mode 100644 index 0000000..d7ad264 Binary files /dev/null and b/modules/chapter3/images/custom_image_workbench.png differ diff --git a/modules/chapter3/images/custom_image_workbench1.png b/modules/chapter3/images/custom_image_workbench1.png new file mode 100644 index 0000000..53492fc Binary files /dev/null and b/modules/chapter3/images/custom_image_workbench1.png differ diff --git a/modules/chapter3/images/custom_image_workbench2.png b/modules/chapter3/images/custom_image_workbench2.png new file mode 100644 index 0000000..c802ab2 Binary files /dev/null and b/modules/chapter3/images/custom_image_workbench2.png differ diff --git a/modules/chapter3/images/custom_image_workbench3.png b/modules/chapter3/images/custom_image_workbench3.png new file mode 100644 index 0000000..037f8d7 Binary files /dev/null and b/modules/chapter3/images/custom_image_workbench3.png differ diff --git a/modules/chapter3/pages/importcustom.adoc b/modules/chapter3/pages/importcustom.adoc index b3b0fbd..6d960e7 100644 --- a/modules/chapter3/pages/importcustom.adoc +++ b/modules/chapter3/pages/importcustom.adoc @@ -41,34 +41,34 @@ For more information, refer to https://docs.openshift.com/container-platform/lat 3. Login to the OpenShift AI dashboard as the admin user. Expand *Settings*, click *Notebook Images*, and then click *Import Image*. + -image::rhodsAdminCustomNotebook.png[OpenShift AI Admin Notebook Images] +image::custom_image_notebook3.png[OpenShift AI Admin Notebook Images] 4. Enter the path to the your repository and give the image a name. Optionally add a description and package information. Click the *Import* button. + -image::rhodsImportCustomImage.png[OpenShift AI Import Notebook] +image::custom_image_notebook2.png[OpenShift AI Import Notebook] + You should now see the image in the list and enabled. + -image::rhodsNotebookImage.png[OpenShift AI Custom Notebooks] +image::custom_image_notebook1.png[OpenShift AI Custom Notebooks] == Test the Image in a Workbench 1. Now we'll test out the image we just imported. Go to *Data Science Projects* and then click the *Create data science project* button. + -image::rhodsDataScienceProj.png[OpenShift AI Data Science Projects] +image::create_dsp_project.png[OpenShift AI Data Science Projects] 2. Give the project a name and resource name. Click the *Create* button. + -image::rhodsCreateProj.png[OpenShift AI Project] +image::custom_image_workbench2.png[OpenShift AI Project] 3. Click the *Create* button. // This is covered in the earlier instruction. + -image::rhodsCreateWrkbench.png[OpenShift AI Workbench] +image::custom_image_workbench1.png[OpenShift AI Workbench] 4. Give the workbench a name and select the rhods-admin-custom-image from the Image selection drop down list. Click the *Create Workbench* button. + -image::rhodsCreateWrkbenchDetails.png[OpenShift AI Workbench Details] +image::custom_image_workbench.png[OpenShift AI Workbench Details] 5. Once the Status changes to Running click the *Open* link. +