Chapter 7. Installing and managing Red Hat OpenShift Data Science components

Procedure

1. Open a new terminal window.

2. In the OpenShift command-line interface (CLI), log in to your on your OpenShift Container Platform cluster as a cluster administrator, as shown in the following example:

   $ oc login <openshift_cluster_url> -u <admin_username> -p <password>

3. Create a DataScienceCluster object custom resource (CR) file, for example, rhods-operator-dsc.yaml.

   apiVersion: datasciencecluster.opendatahub.io/v1
   kind: DataScienceCluster
   metadata:
     name: default-dsc
   spec:
     components:
       codeflare:
         managementState: "Removed"
       dashboard:
         managementState: "Removed"
       datasciencepipelines:
         managementState: "Removed"
       kserve:
         managementState: "Removed"
       modelmeshserving:
         managementState: "Removed"
       ray:
         managementState: "Removed"
       workbenches:
         managementState: "Removed"

4. In the spec.components section of the CR, for each OpenShift Data Science component shown, set the value of the managementState field to either Managed or Removed. These values are defined as follows:

   Managed

   The Operator actively manages the component, installs it, and tries to keep it active. The Operator will upgrade the component only if it is safe to do so.

   Removed

   The Operator actively manages the component but does not install it. If the component is already installed, the Operator will try to remove it.

   Important
   • The KServe component is part of a composite model-serving stack that is a Limited Availability feature. Limited Availability means that you can install and receive support for the feature only with specific approval from Red Hat. Without such approval, the feature is unsupported. Before enabling the KServe component, you must install some other, dependent Operators, including the Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh Operators. For more information, see Serving large language models.
   • You cannot install both the KServe and Model Mesh Serving components.
   • The CodeFlare and KubeRay components are Technology Preview features only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them 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 Red Hat Technology Preview features, see Technology Preview Features Support Scope.
   • To learn how to configure the distributed workloads feature that uses the CodeFlare and KubeRay components, see Configuring distributed workloads.

5. Create the DataScienceCluster object in your OpenShift Container Platform cluster to install the specified OpenShift Data Science components.

   $ oc create -f rhods-operator-dsc.yaml

   You see output similar to the following:

   datasciencecluster.datasciencecluster.opendatahub.io/default created

Procedure

1. Log in to the OpenShift Container Platform web console as a cluster administrator.
2. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift Data Science Operator.

3. Create a DataScienceCluster object to install OpenShift Data Science components by performing the following actions:

   1. Click the Data Science Cluster tab.
   2. Click Create DataScienceCluster.

   3. For Configure via, select YAML view.

      An embedded YAML editor opens showing a default custom resource (CR) for the DataScienceCluster object.

   4. In the spec.components section of the CR, for each OpenShift Data Science component shown, set the value of the managementState field to either Managed or Removed. These values are defined as follows:

      Managed

      The Operator actively manages the component, installs it, and tries to keep it active. The Operator will upgrade the component only if it is safe to do so.

      Removed

      The Operator actively manages the component but does not install it. If the component is already installed, the Operator will try to remove it.

      Important
      • The KServe component is part of a composite model-serving stack that is a Limited Availability feature. Limited Availability means that you can install and receive support for the feature only with specific approval from Red Hat. Without such approval, the feature is unsupported. Before enabling this component, you must install some other, dependent Operators, including the Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh Operators. For more information, see Serving large language models.
      • You cannot install both the KServe and Model Mesh Serving components.
      • The CodeFlare and KubeRay components are Technology Preview features only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them 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 Red Hat Technology Preview features, see Technology Preview Features Support Scope.
      • To learn how to configure the distributed workloads feature that uses the CodeFlare and KubeRay components, see Configuring distributed workloads.
4. Click Create.

Procedure

1. Log in to the OpenShift Container Platform web console as a cluster administrator.
2. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift Data Science Operator.
3. Click the Data Science Cluster tab.
4. On the DataScienceClusters page, click the default object.

5. Click the YAML tab.

   An embedded YAML editor opens showing the custom resource (CR) file for the DataScienceCluster object.

6. In the spec.components section of the CR, for each OpenShift Data Science component shown, set the value of the managementState field to either Managed or Removed. These values are defined as follows:

   Managed

   The Operator actively manages the component, installs it, and tries to keep it active. The Operator will upgrade the component only if it is safe to do so.

   Removed

   The Operator actively manages the component but does not install it. If the component is already installed, the Operator will try to remove it.

   Important
   • If they are not already present in the CR file, you can install the CodeFlare, KServe, and KubeRay features by adding components called codeflare, kserve, and ray to the spec.components section of the CR and setting the managementState field for the components to Managed.
   • The KServe component is part of a composite model-serving stack that is a Limited Availability feature. Limited Availability means that you can install and receive support for the feature only with specific approval from Red Hat. Without such approval, the feature is unsupported. Before enabling this component, you must install some other, dependent Operators, including the Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh Operators. For more information, see Serving large language models.
   • You cannot install both the KServe and Model Mesh Serving components.
   • The CodeFlare and KubeRay components are Technology Preview features only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them 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 Red Hat Technology Preview features, see Technology Preview Features Support Scope.
   • To learn how to configure the distributed workloads feature that uses the CodeFlare and KubeRay components, see Configuring distributed workloads.
7. Click Save.