Container Service for Kubernetes:Deploy the cloud-native AI suite

Deploy the cloud-native AI suite

1. Log on to the ACK console. In the left-side navigation pane, click Clusters.

2. On the Clusters page, find the cluster that you want to manage and click its name. In the left-side pane, choose Applications > Cloud-native AI Suite.

3. On the Cloud-native AI Suite page, click Deploy. On the page that appears, select the components that you want to install.

   The following table describes how to configure the parameters. The following table also describes the components and the supported cluster types.

   Configuration in the console		Component configuration		Supported cluster
   Configuration item	Description	Component name and description	Namespace	ACK Pro cluster	ACK Serverless Pro cluster	ACK Edge Pro cluster
   Elasticity	Specify whether to enable the elastic feature. For more information, see Kubernetes-based model training jobs and Containerized elastic inference services.	ack-alibaba-cloud-metrics-adapter, an auto scaling component.	kube-system
   Acceleration	Specify whether to enable the Fluid Data Acceleration feature. For more information, see Overview of Fluid.	ack-fluid, a data caching and acceleration component.	fluid-system
   Scheduling	Specify whether to enable the Scheduling Component (Batch Task Scheduling, GPU Sharing, Topology-aware GPU scheduling, and NPU scheduling) feature. You can click Advanced to configure custom parameters.	ack-ai-installer, a scheduling component.	kube-system
   	Specify whether to enable the Kube Queue feature. For more information, see Use ack-kube-queue to manage job queues.	ack-kube-queue, a scheduling component that manages workloads in Kubernetes.	kube-queue
   Interaction Mode	Arena: Select this option if you want to use the Arena CLI. To use the Arena CLI, you also need to install and configure the Arena client. After you install the Arena client, you can use the Arena CLI to integrate Kubeflow training operators. You can click Advanced to configure custom parameters. If you select Kube Queue, Console, and Kubeflow Pipelines at the same time, the Arena option is required. For more information, see Configure the Arena client.	ack-arena (ecosystem tool), a machine learning CLI.	kube-system
   	Console: Deploy a lightweight Platform for AI. You can click Advanced to configure custom parameters.	ack-pai, a lightweight Platform for AI. Recommended. You can integrate algorithms and engines that are deeply optimized by PAI based on years of experience into containerized applications. Services such as Data Science Workshop (DSW), Deep Learning Containers (DLC), and Elastic Algorithm Service (EAS) improve the elasticity and efficiency of AI model development, training, and inference, enhancing effectiveness of training and inference while reducing the barriers to AI development.	pai-system
   	Console: After you select this option to deploy AI Suite Console, configure parameters in the Note dialog box. For more information, see Install and configure AI Dashboard and AI Developer Console.	ack-ai-dashboard (ecosystem tool), a visualized O&M console.	kube-ai
   		ack-ai-dev-console (ecosystem tool), a deep learning development console.	kube-ai
   Console Data Storage	After you set Interaction Mode to Console, set Console Data Storage to Pre-installed MySQL or ApsaraDB RDS. For more information, see Install and configure AI Dashboard and AI Developer Console.	ack-mysql, a MySQL database component.	kube-ai
   Workflow	After you select Kubeflow Pipelines, you can set Workflow Data Storage to Pre-installed MinIO or OSS. For more information, see Install and configure Kubeflow Pipelines.	ack-ai-pipeline (ecosystem tool), a platform for building end-to-end machine learning workflows.	kube-ai
   Monitoring	Specify whether to install the Monitoring Component. For more information, see Work with cloud-native AI dashboards.	ack-arena-exporter, a cluster monitoring component.	kube-ai

4. Click Deploy Cloud-native AI Suite in the lower part of the page. The system checks the environment and the dependencies of the selected components. After the environment and the dependencies pass the check, the system deploys the selected components.

   After the components are installed, you can view the following information in the Components list:

   • You can view the names and versions of the components that are installed in the cluster. You can deploy or uninstall components.

   • If a component is updatable, you can update the component.

   • After you install ack-ai-dashboard and ack-ai-dev-console, you can find the hyperlinks to AI Dashboard and AI Developer Console in the upper-left corner of the Cloud-native AI Suite page. You can click a hyperlink to access the corresponding component.

5. After the installation is complete, you can find the hyperlinks to AI Dashboard and AI Developer Console in the upper-left corner of the Cloud-native AI Suite page. You can click a hyperlink to access the corresponding component.

Install and configure AI Dashboard and AI Developer Console

1. In the Interaction Mode section of the Cloud-native AI Suite page, select Console. The Note dialog box appears, as shown in the following figure.

   • If Authorized is displayed, perform Step 3.

   • If Unauthorized is displayed in red and the OK button is dimmed, perform Step 2.

     

2. Create a custom policy to grant permissions to the RAM worker role.

   1. Create a custom policy.

      1. Log on to the RAM console. In the left-side navigation pane, choose Permissions > Policies.

      2. On the Policies page, click Create Policy.

      3. Click the JSON tab. Add the following content to the Action field and click Next to edit policy information.

          "log:GetProject",
          "log:GetLogStore",
          "log:GetConfig",
          "log:GetMachineGroup",
          "log:GetAppliedMachineGroups",
          "log:GetAppliedConfigs",
          "log:GetIndex",
          "log:GetSavedSearch",
          "log:GetDashboard",
          "log:GetJob",
          "ecs:DescribeInstances",
          "ecs:DescribeSpotPriceHistory",
          "ecs:DescribePrice",
          "eci:DescribeContainerGroups",
          "eci:DescribeContainerGroupPrice",
          "log:GetLogStoreLogs",
          "ims:CreateApplication",
          "ims:UpdateApplication",
          "ims:GetApplication",
          "ims:ListApplications",
          "ims:DeleteApplication",
          "ims:CreateAppSecret",
          "ims:GetAppSecret",
          "ims:ListAppSecretIds",
          "ims:ListUsers"

      4. Specify the Name parameter in the k8sWorkerRolePolicy-{ClusterID} format and click OK.

   2. Grant permissions to the RAM worker role of the cluster.

      1. Log on to the RAM console. In the left-side navigation pane, choose Identities > Roles.

      2. Enter the RAM worker role in the KubernetesWorkerRole-{ClusterID} format into the search box. Find the role that you want to manage and click Grant Permission in the Actions column.

      3. In the Select Policy section, click Custom Policy.

      4. Enter the name of the custom policy that you created in the k8sWorkerRolePolicy-{ClusterID} format into the search box and select the policy.

      5. Click OK.

   3. Return to the Note dialog box and click Authorization Check. If the authorization is successful, Authorized is displayed and the OK button becomes available. Then, perform Step 3.

      

3. Select a method to access AI Dashboard and a method to access AI Developer Console. Then, click OK.

   You can use a private IP address, internal domain name, or public domain name to access AI Dashboard or AI Developer Console.

   • In the production environment, use private IPs or public domain names to access services.

   • The public domain name method is recommended only for testing. If you need to use a public domain name, bind it with the public IP of the cluster's Nginx Ingree SLB in your local Host file.

   Note

   • If you want to use a private IP address to access AI Dashboard or AI Developer Console, select Private IP in the Note dialog box.

   • For more information about how to use a private IP address or internal domain name to access AI Dashboard or AI Developer Console, see Access AI Dashboard.

4. Set the Console Data Storage parameter.

   After you select Console, the Console Data Storage parameter appears below the Interaction Mode parameter. You can select Pre-installed MySQL or ApsaraDB RDS.

   

   Pre-installed MySQL

   If you do not select ApsaraDB RDS, Pre-installed MySQL is used by default. Due to stability and SLA issues, we recommend that you use this method only for testing. If you select this method, a new disk is created and billed each time you deploy the cloud-native AI suite. You need to manually release the disks.

   Important

   If the cluster or storage fails, data may be lost.

   When you deploy the cloud-native AI suite, ACK creates a persistent volume claim (PVC) based on the specified StorageClass, and creates and mounts a disk whose size is 120 GB to persist MySQL data. You are charged for the disk and must manage the disk. If you no longer use the disk, release the disk at the earliest opportunity. For more information about how to release a disk, see Release a disk.

   ApsaraDB RDS

   Note

   • If connection issues occur when you use ApsaraDB RDS, refer to Troubleshoot failures in connecting to an ApsaraDB RDS for MySQL instance.

   • If you want to change the method to store data, you must uninstall and redeploy the cloud-native AI suite. If a Secret named kubeai-rds already exists, delete the Secret by using kubectl.

   1. Purchase an ApsaraDB RDS instance and create a database and account. For more information, see Getting Started. For more information about the billing of ApsaraDB RDS, see Billing overview.

   2. Click Deploy Cloud-native AI Suite in the lower part of the page to install the cloud-native AI suite.

   3. Click the name of the cluster and choose Configurations > Secrets in the left-side navigation pane.

   4. Select kube-ai from the Namespace drop-down list on the top of the page.

   5. Click Create from YAML in the upper-right corner of the page.

   6. Copy the following YAML content to create a Secret named kubeai-rds:

      apiVersion: v1
      kind: Secret
      metadata:
        name: kubeai-rds
        namespace: kube-ai
      type: Opaque
      stringData:
        MYSQL_HOST: "Your RDS URL"
        MYSQL_DB_NAME: "Database name"
        MYSQL_USER: "Database username"
        MYSQL_PASSWORD: "Database password"

      Parameter	Description
      name	The name of the Secret.
      namespace	The name of the namespace.
      MYSQL_HOST MYSQL_DB_NAME MYSQL_USER MYSQL_PASSWORD	The parameters related to the ApsaraDB RDS for MySQL instance. For more information, see Create an ApsaraDB RDS for MySQL instance and Create accounts and databases.

Install and configure Kubeflow Pipelines