Chapter 3. Installing and deploying OpenShift AI Red Hat OpenShift AI is a platform for data scientists and developers of artificial intelligence (AI) applications. It provides a fully supported environment that lets you rapidly develop, train, test, and deploy machine learning models on-premises and/or in the public cloud. OpenShift AI is provided as a managed cloud service add-on for Red Hat OpenShift or as self-managed software that you can install on-premise or in the public cloud on OpenShift. For information about installing OpenShift AI as self-managed software on your OpenShift cluster in a disconnected environment, see Installing and uninstalling OpenShift AI Self-Managed in a disconnected environment . For information about installing OpenShift AI as a managed cloud service add-on, see Installing and uninstalling OpenShift AI Cloud Service . Installing OpenShift AI involves the following high-level tasks: Confirm that your OpenShift cluster meets all requirements. See Requirements for OpenShift AI Self-Managed . Add administrative users for OpenShift. See Adding administrative users in OpenShift . Install the Red Hat OpenShift AI Operator. See Installing the Red Hat OpenShift AI Operator . Install OpenShift AI components. See Installing and managing Red Hat OpenShift AI components . Configure user and administrator groups to provide user access to OpenShift AI. See Adding users to OpenShift AI user groups . Access the OpenShift AI dashboard. See Accessing the OpenShift AI dashboard . Optionally, configure and enable your accelerators in OpenShift AI to ensure that your data scientists can use compute-heavy workloads in their models. See Enabling accelerators . 3.1. Requirements for OpenShift AI Self-Managed You must meet the following requirements before you can install Red Hat OpenShift AI on your Red Hat OpenShift cluster: Product subscriptions You must have a subscription for Red Hat OpenShift AI Self-Managed. If you want to install OpenShift AI Self-Managed in a Red Hat-managed cloud environment, you must have a subscription for one of the following platforms: Red Hat OpenShift Dedicated on Amazon Web Services (AWS) or Google Cloud Platform (GCP) Red Hat OpenShift Service on Amazon Web Services (ROSA Classic) Red Hat OpenShift Service on Amazon Web Services with hosted control planes (ROSA HCP) Microsoft Azure Red Hat OpenShift Contact your Red Hat account manager to purchase new subscriptions. If you do not yet have an account manager, complete the form at https://www.redhat.com/en/contact to request one. Cluster administrator access to your OpenShift cluster You must have an OpenShift cluster with cluster administrator access. Use an existing cluster, or create a cluster by following the steps in the relevant documentation: OpenShift Container Platform 4.14 or later: OpenShift Container Platform installation overview OpenShift Dedicated: Creating an OpenShift Dedicated cluster ROSA Classic: Install ROSA Classic clusters ROSA HCP: Install ROSA with HCP clusters Your cluster must have at least 2 worker nodes with at least 8 CPUs and 32 GiB RAM available for OpenShift AI to use when you install the Operator. To ensure that OpenShift AI is usable, additional cluster resources are required beyond the minimum requirements. To use OpenShift AI on single node OpenShift, the node has to have at least 32 CPUs and 128 GiB RAM. Your cluster is configured with a default storage class that can be dynamically provisioned. Confirm that a default storage class is configured by running the oc get storageclass command. If no storage classes are noted with (default) beside the name, follow the OpenShift Container Platform documentation to configure a default storage class: Changing the default storage class . For more information about dynamic provisioning, see Dynamic provisioning . Open Data Hub must not be installed on the cluster. For more information about managing the machines that make up an OpenShift cluster, see Overview of machine management . An identity provider configured for OpenShift Red Hat OpenShift AI uses the same authentication systems as Red Hat OpenShift Container Platform. See Understanding identity provider configuration for more information on configuring identity providers. Access to the cluster as a user with the cluster-admin role; the kubeadmin user is not allowed. Internet access Along with Internet access, the following domains must be accessible during the installation of OpenShift AI Self-Managed: cdn.redhat.com subscription.rhn.redhat.com registry.access.redhat.com registry.redhat.io quay.io For CUDA-based images, the following domains must be accessible: ngc.download.nvidia.cn developer.download.nvidia.com Create custom namespaces By default, OpenShift AI uses predefined namespaces, but you can define a custom namespace for the operator and DSCI.applicationNamespace as needed. Namespaces created by OpenShift AI typically include openshift or redhat in their name. Do not rename these system namespaces because they are required for OpenShift AI to function properly. If you are using custom namespaces, before installing the OpenShift AI Operator, you must have created and labeled them as required. Data science pipelines preparation Data science pipelines 2.0 contains an installation of Argo Workflows. If there is an existing installation of Argo Workflows that is not installed by data science pipelines on your cluster, data science pipelines will be disabled after you install OpenShift AI. Before installing OpenShift AI, ensure that your cluster does not have an existing installation of Argo Workflows that is not installed by data science pipelines, or remove the separate installation of Argo Workflows from your cluster. You can store your pipeline artifacts in an S3-compatible object storage bucket so that you do not consume local storage. To do this, you must first configure write access to your S3 bucket on your storage account. Install KServe dependencies To support the KServe component, which is used by the single-model serving platform to serve large models, you must also install Operators for Red Hat OpenShift Serverless and Red Hat OpenShift Service Mesh and perform additional configuration. For more information, see About the single-model serving platform . If you want to add an authorization provider for the single-model serving platform, you must install the Red Hat - Authorino Operator. For information, see Adding an authorization provider for the single-model serving platform . Install model registry dependencies (Technology Preview feature) To use the model registry component, you must also install Operators for Red Hat Authorino, Red Hat OpenShift Serverless, and Red Hat OpenShift Service Mesh. For more information about configuring the model registry component, see Configuring the model registry component . Access to object storage Components of OpenShift AI require or can use S3-compatible object storage such as AWS S3, MinIO, Ceph, or IBM Cloud Storage. An object store is a data storage mechanism that enables users to access their data either as an object or as a file. The S3 API is the recognized standard for HTTP-based access to object storage services. Object storage is required for the following components: Single- or multi-model serving platforms, to deploy stored models. See Deploying models on the single-model serving platform or Deploying a model by using the multi-model serving platform . Data science pipelines, to store artifacts, logs, and intermediate results. See Configuring a pipeline server and About pipeline logs . Object storage can be used by the following components: Workbenches, to access large datasets. See Adding a connection to your data science project . Distributed workloads, to pull input data from and push results to. See Running distributed data science workloads from data science pipelines . Code executed inside a pipeline. For example, to store the resulting model in object storage. See Overview of pipelines in Jupyterlab . 3.2. Adding administrative users in OpenShift Before you can install and configure OpenShift AI for your data scientist users, you must obtain OpenShift cluster administrator ( cluster-admin ) privileges. To assign cluster-admin privileges to a user, follow the steps in the relevant OpenShift documentation: OpenShift Container Platform: Creating a cluster admin OpenShift Dedicated: Managing OpenShift Dedicated administrators ROSA: Creating a cluster administrator user for quick cluster access 3.3. Configuring custom namespaces By default, OpenShift AI uses predefined namespaces, but you can define a custom namespace for the operator and DSCI.applicationNamespace as needed. Namespaces created by OpenShift AI typically include openshift or redhat in their name. Do not rename these system namespaces because they are required for OpenShift AI to function properly. Prerequisites You have access to a OpenShift AI cluster with cluster administrator privileges. You have downloaded and installed the OpenShift command-line interface (CLI). See Installing the OpenShift CLI . Procedure In a terminal window, if you are not already logged in to your OpenShift cluster as a cluster administrator, log in to the OpenShift CLI as shown in the following example: Enter the following command to create the custom namespace: If you are creating a namespace for a DSCI.applicationNamespace , enter the following command to add the correct label: 3.4. Installing the Red Hat OpenShift AI Operator This section shows how to install the Red Hat OpenShift AI Operator on your OpenShift cluster using the command-line interface (CLI) and the OpenShift web console. Note If you want to upgrade from a version of OpenShift AI rather than performing a new installation, see Upgrading OpenShift AI . Note If your OpenShift cluster uses a proxy to access the Internet, you can configure the proxy settings for the Red Hat OpenShift AI Operator. See Overriding proxy settings of an Operator for more information. 3.4.1. Installing the Red Hat OpenShift AI Operator by using the CLI The following procedure shows how to use the OpenShift command-line interface (CLI) to install the Red Hat OpenShift AI Operator on your OpenShift cluster. You must install the Operator before you can install OpenShift AI components on the cluster. Prerequisites You have a running OpenShift cluster, version 4.14 or greater, configured with a default storage class that can be dynamically provisioned. You have cluster administrator privileges for your OpenShift cluster. You have downloaded and installed the OpenShift command-line interface (CLI). See Installing the OpenShift CLI . Procedure Open a new terminal window. Follow these steps to log in to your OpenShift cluster as a cluster administrator: In the upper-right corner of the OpenShift web console, click your user name and select Copy login command . After you have logged in, click Display token . Copy the Log in with this token command and paste it in the OpenShift command-line interface (CLI). Create a namespace for installation of the Operator by performing the following actions: Create a namespace YAML file named rhods-operator-namespace.yaml . apiVersion: v1 kind: Namespace metadata: name: redhat-ods-operator 1 1 Defines the required redhat-ods-operator namespace for installation of the Operator. Create the namespace in your OpenShift cluster. You see output similar to the following: Create an operator group for installation of the Operator by performing the following actions: Create an OperatorGroup object custom resource (CR) file, for example, rhods-operator-group.yaml . apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: rhods-operator namespace: redhat-ods-operator 1 1 Defines the required redhat-ods-operator namespace. Create the OperatorGroup object in your OpenShift cluster. You see output similar to the following: Create a subscription for installation of the Operator by performing the following actions: Create a Subscription object CR file, for example, rhods-operator-subscription.yaml . apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: rhods-operator namespace: redhat-ods-operator 1 spec: name: rhods-operator channel: <channel> 2 source: redhat-operators sourceNamespace: openshift-marketplace startingCSV: rhods-operator.x.y.z 3 1 Defines the required redhat-ods-operator namespace. 2 Sets the update channel. You must specify a value of fast , stable , stable-x.y eus-x.y , or alpha . For more information, see Understanding update channels . 3 Optional: Sets the operator version. If you do not specify a value, the subscription defaults to the latest operator version. For more information, see the Red Hat OpenShift AI Self-Managed Life Cycle Knowledgebase article. Create the Subscription object in your OpenShift cluster to install the Operator. You see output similar to the following: Verification In the OpenShift web console, click Operators Installed Operators and confirm that the Red Hat OpenShift AI Operator shows one of the following statuses: Installing - installation is in progress; wait for this to change to Succeeded . This might take several minutes. Succeeded - installation is successful. In the web console, click Home Projects and confirm that the following project namespaces are visible and listed as Active : redhat-ods-applications redhat-ods-monitoring redhat-ods-operator Additional resources Installing and managing Red Hat OpenShift AI components Adding users to OpenShift AI user groups . Adding Operators to a cluster 3.4.2. Installing the Red Hat OpenShift AI Operator by using the web console The following procedure shows how to use the OpenShift web console to install the Red Hat OpenShift AI Operator on your cluster. You must install the Operator before you can install OpenShift AI components on the cluster. Prerequisites You have a running OpenShift cluster, version 4.14 or greater, configured with a default storage class that can be dynamically provisioned. You have cluster administrator privileges for your OpenShift cluster. Procedure Log in to the OpenShift web console as a cluster administrator. In the web console, click Operators OperatorHub . On the OperatorHub page, locate the Red Hat OpenShift AI Operator by scrolling through the available Operators or by typing Red Hat OpenShift AI into the Filter by keyword box. Click the Red Hat OpenShift AI tile. The Red Hat OpenShift AI information pane opens. Select a Channel . For information about subscription update channels, see Understanding update channels . Select a Version . Click Install . The Install Operator page opens. Review or change the selected channel and version as needed. For Installation mode , note that the only available value is All namespaces on the cluster (default) . This installation mode makes the Operator available to all namespaces in the cluster. For Installed Namespace , select Operator recommended Namespace: redhat-ods-operator . For Update approval , select one of the following update strategies: Automatic : New updates in the update channel are installed as soon as they become available. Manual : A cluster administrator must approve any new updates before installation begins. Important By default, the Red Hat OpenShift AI Operator follows a sequential update process. This means that if there are several versions between the current version and the target version, Operator Lifecycle Manager (OLM) upgrades the Operator to each of the intermediate versions before it upgrades it to the final, target version. If you configure automatic upgrades, OLM automatically upgrades the Operator to the latest available version. If you configure manual upgrades, a cluster administrator must manually approve each sequential update between the current version and the final, target version. For information about supported versions, see the Red Hat OpenShift AI Life Cycle Knowledgebase article. Click Install . The Installing Operators pane appears. When the installation finishes, a checkmark appears to the Operator name. Verification In the OpenShift web console, click Operators Installed Operators and confirm that the Red Hat OpenShift AI Operator shows one of the following statuses: Installing - installation is in progress; wait for this to change to Succeeded . This might take several minutes. Succeeded - installation is successful. In the web console, click Home Projects and confirm that the following project namespaces are visible and listed as Active : redhat-ods-applications redhat-ods-monitoring redhat-ods-operator Additional resources Installing and managing Red Hat OpenShift AI components Adding users to OpenShift AI user groups Adding Operators to a cluster 3.5. Installing and managing Red Hat OpenShift AI components You can use the OpenShift command-line interface (CLI) or OpenShift web console to install and manage components of Red Hat OpenShift AI on your OpenShift cluster. 3.5.1. Installing Red Hat OpenShift AI components by using the CLI To install Red Hat OpenShift AI components by using the OpenShift command-line interface (CLI), you must create and configure a DataScienceCluster object. Important The following procedure describes how to create and configure a DataScienceCluster object to install Red Hat OpenShift AI components as part of a new installation. For information about changing the installation status of OpenShift AI components after installation, see Updating the installation status of Red Hat OpenShift AI components by using the web console . For information about upgrading OpenShift AI, see Upgrading OpenShift AI Self-Managed . Prerequisites The Red Hat OpenShift AI Operator is installed on your OpenShift cluster. See Installing the Red Hat OpenShift AI Operator . You have cluster administrator privileges for your OpenShift cluster. You have downloaded and installed the OpenShift command-line interface (CLI). See Installing the OpenShift CLI . Procedure Open a new terminal window. Follow these steps to log in to your OpenShift cluster as a cluster administrator: In the upper-right corner of the OpenShift web console, click your user name and select Copy login command . After you have logged in, click Display token . Copy the Log in with this token command and paste it in the OpenShift command-line interface (CLI). 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 1 2 kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed 1 To fully install the KServe component, which is used by the single-model serving platform to serve large models, you must install Operators for Red Hat OpenShift Service Mesh and Red Hat OpenShift Serverless and perform additional configuration. See Installing the single-model serving platform . 2 If you have not enabled the KServe component (that is, you set the value of the managementState field to Removed ), you must also disable the dependent Service Mesh component to avoid errors. See Disabling KServe dependencies . In the spec.components section of the CR, for each OpenShift AI 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 To learn how to fully install the KServe component, which is used by the single-model serving platform to serve large models, see Installing the single-model serving platform . If you have not enabled the KServe component (that is, you set the value of the managementState field to Removed ), you must also disable the dependent Service Mesh component to avoid errors. See Disabling KServe dependencies . To learn how to install the distributed workloads components, see Installing the distributed workloads components . Create the DataScienceCluster object in your OpenShift cluster to install the specified OpenShift AI components. You see output similar to the following: Verification Confirm that there is a running pod for each component: In the OpenShift web console, click Workloads Pods . In the Project list at the top of the page, select redhat-ods-applications . In the applications namespace, confirm that there are running pods for each of the OpenShift AI components that you installed. Confirm the status of all installed components: In the OpenShift web console, click Operators Installed Operators . Click the Red Hat OpenShift AI Operator. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc . Select the YAML tab. In the installedComponents section, confirm that the components you installed have a status value of true . Note If a component shows with the component-name: {} format in the spec.components section of the CR, the component is not installed. 3.5.2. Installing Red Hat OpenShift AI components by using the web console To install Red Hat OpenShift AI components by using the OpenShift web console, you must create and configure a DataScienceCluster object. Important The following procedure describes how to create and configure a DataScienceCluster object to install Red Hat OpenShift AI components as part of a new installation. For information about changing the installation status of OpenShift AI components after installation, see Updating the installation status of Red Hat OpenShift AI components by using the web console . For information about upgrading OpenShift AI, see Upgrading OpenShift AI Self-Managed . Prerequisites The Red Hat OpenShift AI Operator is installed on your OpenShift cluster. See Installing the Red Hat OpenShift AI Operator . You have cluster administrator privileges for your OpenShift cluster. Procedure Log in to the OpenShift web console as a cluster administrator. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift AI Operator. Click the Data Science Cluster tab. Click Create DataScienceCluster . For Configure via , select YAML view . An embedded YAML editor opens showing a default custom resource (CR) for the DataScienceCluster object, similar to the following example: 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 1 2 kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed 1 To fully install the KServe component, which is used by the single-model serving platform to serve large models, you must install Operators for Red Hat OpenShift Service Mesh and Red Hat OpenShift Serverless and perform additional configuration. See Installing the single-model serving platform . 2 If you have not enabled the KServe component (that is, you set the value of the managementState field to Removed ), you must also disable the dependent Service Mesh component to avoid errors. See Disabling KServe dependencies . In the spec.components section of the CR, for each OpenShift AI 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 To learn how to fully install the KServe component, which is used by the single-model serving platform to serve large models, see Installing the single-model serving platform . If you have not enabled the KServe component (that is, you set the value of the managementState field to Removed ), you must also disable the dependent Service Mesh component to avoid errors. See Disabling KServe dependencies . To learn how to install the distributed workloads components, see Installing the distributed workloads components . Click Create . Verification Confirm that there is a running pod for each component: In the OpenShift web console, click Workloads Pods . In the Project list at the top of the page, select redhat-ods-applications . In the applications namespace, confirm that there are running pods for each of the OpenShift AI components that you installed. Confirm the status of all installed components: In the OpenShift web console, click Operators Installed Operators . Click the Red Hat OpenShift AI Operator. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc . Select the YAML tab. In the installedComponents section, confirm that the components you installed have a status value of true . Note If a component shows with the component-name: {} format in the spec.components section of the CR, the component is not installed. 3.5.3. Updating the installation status of Red Hat OpenShift AI components by using the web console You can use the OpenShift web console to update the installation status of components of Red Hat OpenShift AI on your OpenShift cluster. Important If you upgraded OpenShift AI, the upgrade process automatically used the values of the version's DataScienceCluster object. New components are not automatically added to the DataScienceCluster object. After upgrading OpenShift AI: Inspect the default DataScienceCluster object to check and optionally update the managementState status of the existing components. Add any new components to the DataScienceCluster object. Prerequisites The Red Hat OpenShift AI Operator is installed on your OpenShift cluster. You have cluster administrator privileges for your OpenShift cluster. Procedure Log in to the OpenShift web console as a cluster administrator. In the web console, click Operators Installed Operators and then click the Red Hat OpenShift AI Operator. Click the Data Science Cluster tab. On the DataScienceClusters page, click the default object. Click the YAML tab. An embedded YAML editor opens showing the default custom resource (CR) for the DataScienceCluster object, similar to the following example: 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 kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed In the spec.components section of the CR, for each OpenShift AI 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 To learn how to install the KServe component, which is used by the single-model serving platform to serve large models, see Installing the single-model serving platform . If you have not enabled the KServe component (that is, you set the value of the managementState field to Removed ), you must also disable the dependent Service Mesh component to avoid errors. See Disabling KServe dependencies . To learn how to install the distributed workloads feature, see Installing the distributed workloads components . Click Save . For any components that you updated, OpenShift AI initiates a rollout that affects all pods to use the updated image. Verification Confirm that there is a running pod for each component: In the OpenShift web console, click Workloads Pods . In the Project list at the top of the page, select redhat-ods-applications . In the applications namespace, confirm that there are running pods for each of the OpenShift AI components that you installed. Confirm the status of all installed components: In the OpenShift web console, click Operators Installed Operators . Click the Red Hat OpenShift AI Operator. Click the Data Science Cluster tab and select the DataScienceCluster object called default-dsc . Select the YAML tab. In the installedComponents section, confirm that the components you installed have a status value of true . Note If a component shows with the component-name: {} format in the spec.components section of the CR, the component is not installed.

Commands:
login <openshift_cluster_url> -u <admin_username> -p <password>
create namespace <custom_namespace>
label namespace <application_namespace> opendatahub.io/application-namespace=true
oc login --token= <token> --server= <openshift_cluster_url>
apiVersion: v1 kind: Namespace metadata: name: redhat-ods-operator 1
oc create -f rhods-operator-namespace.yaml
namespace/redhat-ods-operator created
apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: rhods-operator namespace: redhat-ods-operator 1
oc create -f rhods-operator-group.yaml
operatorgroup.operators.coreos.com/rhods-operator created
apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: rhods-operator namespace: redhat-ods-operator 1 spec: name: rhods-operator channel: <channel> 2 source: redhat-operators sourceNamespace: openshift-marketplace startingCSV: rhods-operator.x.y.z 3
oc create -f rhods-operator-subscription.yaml
subscription.operators.coreos.com/rhods-operator created
oc login --token= <token> --server= <openshift_cluster_url>
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 1 2 kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed
oc create -f rhods-operator-dsc.yaml
datasciencecluster.datasciencecluster.opendatahub.io/default created
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 1 2 kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed
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 kueue: managementState: Removed modelmeshserving: managementState: Removed ray: managementState: Removed trainingoperator: managementState: Removed trustyai: managementState: Removed workbenches: managementState: Removed