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Container Service for Kubernetes:Log on to AI Developer Console

Last Updated:Dec 23, 2025

This topic describes how to add a quota group and associate the quota group with a Resource Access Management (RAM) user in AI Dashboard as a cluster administrator. This topic also describes how to log on to AI Developer Console as a RAM user.

Prerequisites

Step 1: Create a quota group for the RAM user

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

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

  3. Go to the Cloud-native AI Suite page and click AI Dashboard in the upper-left corner.

    After installing ack-ai-dashboard and ack-ai-dev-console, you can directly click AI Dashboard or AI Developer Console in the upper-left corner of the Cloud-native AI Suite page to access the consoles.

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  4. Create a quota group for the RAM user. Skip this step if a quota group already exists.

  5. Associate the quota group with the RAM user. For more information, see Generate a kubeconfig file and a logon token for a newly created user.

    Note

    If no RAM user is available in the User Name drop-down list, log on to the RAM console and create a RAM user. For more information, see Create a RAM user.

  6. On the Cloud-native AI Suite page, click AI Developer Console to obtain the address of AI Developer Console.

Step 2: Log on to AI Developer Console

  1. Open a web browser and enter the URL for the AI Developer Console.

    • If the URL is a public address, you can access it directly.

      Important

      The default public domain provided by ACK for testing purposes is not recommended for production environments as it does not include TLS encryption. For production use, we strongly recommend using your own custom domain and configuring a valid TLS certificate.

    • If the URL is a private address, you must first connect to the private network. See Method 1: Access AI Dashboard over a private network.

  2. Select a logon method.

  3. After successfully logging on, you are redirected to the Overview page of AI Developer Console. You can proceed to deploy your model training jobs as needed.