Chapter 7. Configuring your IDE

You can configure some notebook workbenches to get the most out of your data science work.

7.1. Configuring your code-server workbench

You can use extensions to streamline your workflow, add new languages, themes, debuggers, and connect to additional services.

For more information on code-server, see code-server in GitHub.

Important

The code-server notebook image is currently available in Red Hat OpenShift AI as a Technology Preview feature. 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.

7.1.1. Installing extensions with code-server

Prerequisites

  • You have logged in to Red Hat OpenShift AI.
  • If you use specialized OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins) in OpenShift.
  • You have created a data science project that has a code-server workbench.

Procedure

  1. From the OpenShift AI dashboard, click Data Science Projects.

    The Data Science Projects page opens.

  2. Click the name of the project containing the code-server workbench you want to start.

    A project details page opens.

  3. Click the Workbenches tab.
  4. Click the toggle in the Status column for the relevant workbench to start a workbench that is not running.

    The status of the workbench that you started changes from Stopped to Running.

  5. After the workbench has started, click Open to open the workbench notebook.
  6. In the Activity Bar, click the Extensions icon. ( The Extensions icon )
  7. Search for the name of the extension you want to install.
  8. Click Install to add the extension to your code-server environment.

    The extension you installed appears in the Browser - Installed list on the Extensions panel.

7.1.1.1. Extensions

See Open VSX Registry for available third-party extensions that you can consider installing.

7.2. Building the RStudio Server notebook images

Disclaimer
Red Hat supports managing workbenches in OpenShift AI. However, Red Hat does not provide support for the RStudio software. RStudio Server is available through https://rstudio.org/ and is subject to their licensing terms. Review their licensing terms before you use this sample workbench.

The CUDA - RStudio Server notebook image contains NVIDIA CUDA technology. CUDA licensing information is available at https://docs.nvidia.com/cuda/. Review their licensing terms before you use this sample workbench.

Important

The RStudio Server and CUDA - RStudio Server notebook images are currently available in Red Hat OpenShift AI as Technology Preview features.

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 use the RStudio Server and CUDA - RStudio Server notebook images, you must first build them by creating a secret and triggering the BuildConfig, and then enable them in the OpenShift AI UI by editing the rstudio-rhel9 and cuda-rstudio-rhel9 image streams.

Prerequisites

  • Before starting the RStudio Server build process, you have at least 1 CPU and 2Gi memory available for rstudio-server-rhel9, and 1.5 CPUs and 8Gi memory available for cuda-rstudio-server-rhel9 on your cluster.
  • You are logged in to your OpenShift cluster.
  • You have the cluster-admin role in OpenShift, to the namespace rhoai-applications, or with cluster-wide role binding.
  • You have an active Red Hat Enterprise Linux (RHEL) subscription.

Procedure

  1. Create a secret with Subscription Manager credentials. These are usually your Red Hat Customer Portal username and password.

    Note: The secret must be named rhel-subscription-secret, and its USERNAME and PASSWORD keys must be in capital letters.

    oc create secret generic rhel-subscription-secret --from-literal=USERNAME=<username> --from-literal=PASSWORD=<password> -n redhat-ods-applications
  2. Start the build:

    1. To start the lightweight RStudio Server build:

      oc start-build rstudio-server-rhel9 -n redhat-ods-applications --follow
    2. To start the CUDA-enabled RStudio Server build, trigger the cuda-rhel9 BuildConfig:

      oc start-build cuda-rhel9 -n redhat-ods-applications --follow

      The cuda-rhel9 build is a prerequisite for cuda-rstudio-rhel9. The cuda-rstudio-rhel9 build starts automatically.

  3. Confirm that the build process has completed successfully using the following command. Successful builds appear as Complete.

    oc get builds -n redhat-ods-applications
  4. After the builds complete successfully, use the following commands to make the notebook images available in the OpenShift AI UI.

    1. To enable the RStudio Server notebook image:

      oc label -n redhat-ods-applications imagestream rstudio-rhel9 opendatahub.io/notebook-image='true'
    2. To enable the CUDA - RStudio Server notebook image:

      oc label -n redhat-ods-applications imagestream cuda-rstudio-rhel9 opendatahub.io/notebook-image='true'

Verification

  • You can see RStudio Server and CUDA - RStudio Server images on the ApplicationsEnabled menu in the Red Hat OpenShift AI dashboard.
  • You can see R Studio Server or CUDA - RStudio Server in the Data Science ProjectsWorkbenchesCreate workbenchNotebook imageImage selection dropdown list.