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Deploying a Cloudera Data Visualization application in Cloudera AI

Learn how to deploy Cloudera Data Visualization in Cloudera AI.

Learn how to create a Cloudera AI project with Cloudera Data Visualization Runtime as the default runtime.

If you know that your project will to be running Cloudera Data Visualization, you can add the Cloudera Data Visualization Runtime when setting up the project.
  1. Click Projects on the left sidebar of Cloudera AI Workbench.
  2. Click New Project.
  3. Enter a Project Name.
  4. Optional: You can add a description for your project.
  5. Set the visibility of the project.
    • Private (default) – Only you can access the project.
    • Public – All authenticated users can view the project.
  6. Under Initial Setup, select how you want to create your project.
    You can either create a blank project, or select a source for your project files.
    • Blank – Start with an empty project (no templates, files, or Git sources).
    • Templates – Pre-built example projects in R, Python, PySpark, or Scala to help you get started.
    • APMs – Use Accelerators for ML Projects to to include jobs, models, and experiments.
    • Local Files – Upload an existing project from a compressed file or folder.
    • Git – Clone a Git repository for version control and collaboration.
  7. Configure which Runtime(s) will be available for this particular project.

    Projects are configured with the latest Python and R ML Runtimes. You can change this configuration in the Advanced Options view, where you can add ML Runtimes based on more detailed Editor, Kernel, Edition, and Version criteria.

    1. Enable Advanced Options to customize.
    2. For Cloudera Data Visualization 8.0.0 and higher:
      1. Set the Editor to PBJ Workbench.
      2. Select Cloudera Data Visualization from the Kernel dropdown.
      3. Edition and Version will be automatically set for Cloudera Data Visualization.
    3. For Cloudera Data Visualization versions lower than 8.0.0, set the Editor to Workbench.
      Kernel, Edition, and Version will be automatically set for Cloudera Data Visualization.
    4. Click Add Runtime.
  8. Click Create Project.

After the project is created, you can start creating your application. If you added Cloudera Data Visualization as the only Runtime during setup, it will be the default Runtime when creating applications.

Learn how to add a Cloudera Data Visualization Runtime to an existing Cloudera AI project.

You need to manually add a Cloudera Data Visualization Runtime to your project if the workspace of your project is not set to use the Cloudera Data Visualization Runtime by default.
  1. Click Projects on the left sidebar of Cloudera AI Workbench.
  2. Select the project where you want to add Cloudera Data Visualization Runtime.
  3. Open Project Settings from the left navigation bar.
  4. Click the Runtime tab.
  5. Click Add Runtime.
    The Add new runtime to project modal window opens.
    1. For Cloudera Data Visualization 8.0.0 and higher:
      1. Set the Editor to PBJ Workbench.
      2. Select Cloudera Data Visualization from the Kernel dropdown.
      3. Edition and Version will be automatically set for Cloudera Data Visualization.
    2. For Cloudera Data Visualization versions lower than 8.0.0, set the Editor to Workbench.
      Kernel, Edition, and Version will be automatically set for Cloudera Data Visualization.
  6. Click Submit.

Now that Cloudera Data Visualization Runtime is added, you can select it when creating a Cloudera Data Visualization application.

To proceed, create a Cloudera Data Visualization application.

Learn how to create a Cloudera Data Visualization application in Cloudera AI to help you visualize and interact with your data insights. This integration allows for seamless visualization of ML Model outputs, data exploration, and reporting within the same platform.

Ensure that a Cloudera Data Visualization Runtime is available in the Cloudera AI project where you plan to create the Cloudera Data Visualization application.

For more information about ML Runtimes, see Managing ML Runtimes and Using Runtime Catalog.

  1. Navigate to the Overview page of your Cloudera AI project.
  2. On the left sidebar, click Applications.
  3. Click New Application.
  4. Provide the following details for your new application:
    • Name – Enter a name for the application.
    • Run Application as – If the application is to run in a service account, select Service Account and pick an account from the dropdown.
    • Subdomain – Enter a subdomain that will be used to construct the URL for the web application. Use only URL-friendly characters.
    • Description – Add a description of the application.
    • Script – Use the script located at: /opt/vizapps/tools/arcviz/startup_app.py
    • Runtime – If only one Runtime is available in your project, the fields will be prepopulated. If multiple Runtimes are available, you can select which Runtime to use.
      For Cloudera Data Visualization 8.0.0 and higher
      • Editor – Select PBJ Workbench.
      • Kernel – Select Cloudera Data Visualization.
      • Edition and Version will autopopulate.
      For Cloudera Data Visualization versions lower than 8.0.0
      • Editor – Select Workbench.
      • Kernel, Edition, and Version will autopopulate.
  5. Click Create Application.

After a few minutes, the application status will change from Starting to Running on the Applications page. Your Cloudera Data Visualization application is now ready for use.

You can restart, stop, or delete the application using the options in the supplemental menu. If you want to make changes to the application, navigate to Application Details > Settings.

Start Cloudera Data Visualization.