Chapter 3. Working in code-server

Procedure

1. From the OpenShift AI dashboard, click Data science projects.

   The Data science projects page opens.

2. Click the name of the project that you want to add the workbench to.

   A project details page opens.

3. Click the Workbenches tab.

4. Click Create workbench.

   The Create workbench page opens.

5. In the Name field, enter a unique name for your workbench.

6. Optional: If you want to change the default resource name for your workbench, click Edit resource name.

   The resource name is what your resource is labeled in OpenShift. Valid characters include lowercase letters, numbers, and hyphens (-). The resource name cannot exceed 30 characters, and it must start with a letter and end with a letter or number.

   Note: You cannot change the resource name after the workbench is created. You can edit only the display name and the description.

7. Optional: In the Description field, enter a description for your workbench.

8. In the Workbench image section, complete the fields to specify the workbench image to use with your workbench.

   From the Image selection list, select a workbench image that suits your use case. A workbench image includes an IDE and Python packages (reusable code). If project-scoped images exist, the Image selection list includes subheadings to distinguish between global images and project-scoped images.

   Optionally, click View package information to view a list of packages that are included in the image that you selected.

   If the workbench image has multiple versions available, select the workbench image version to use from the Version selection list. To use the latest package versions, Red Hat recommends that you use the most recently added image.

   Note

   You can change the workbench image after you create the workbench.

9. In the Deployment size section, select one of the following options, depending on whether the hardware profiles feature is enabled.

   Important

   The hardware profiles feature is currently available in Red Hat OpenShift AI 2.21 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.

   • If the hardware profiles feature is not enabled:

     1. From the Container size list, select the appropriate size for the size of the model that you want to train or tune.

        For example, to run the example fine-tuning job described in Fine-tuning a model by using Kubeflow Training, select Medium.

     2. From the Accelerator list, select a suitable accelerator profile for your workbench.

        If project-scoped accelerator profiles exist, the Accelerator list includes subheadings to distinguish between global accelerator profiles and project-scoped accelerator profiles.

   • If the hardware profiles feature is enabled:

     1. From the Hardware profile list, select a suitable hardware profile for your workbench.

        If project-scoped hardware profiles exist, the Hardware profile list includes subheadings to distinguish between global hardware profiles and project-scoped hardware profiles.

        The hardware profile specifies the number of CPUs and the amount of memory allocated to the container, setting the guaranteed minimum (request) and maximum (limit) for both.

     2. If you want to change the default values, click Customize resource requests and limit and enter new minimum (request) and maximum (limit) values.

        Important

        By default, the hardware profiles feature is not enabled: hardware profiles are not shown in the dashboard navigation menu or elsewhere in the user interface. In addition, user interface components associated with the deprecated accelerator profiles functionality are still displayed. To show the Settings Hardware profiles option in the dashboard navigation menu, and the user interface components associated with hardware profiles, set the disableHardwareProfiles value to false in the OdhDashboardConfig custom resource (CR) in OpenShift. For more information, see Dashboard configuration options.

10. Optional: In the Environment variables section, select and specify values for any environment variables.

    Setting environment variables during the workbench configuration helps you save time later because you do not need to define them in the body of your workbenches, or with the IDE command line interface.

    If you are using S3-compatible storage, add these recommended environment variables:

    • AWS_ACCESS_KEY_ID specifies your Access Key ID for Amazon Web Services.
    • AWS_SECRET_ACCESS_KEY specifies your Secret access key for the account specified in AWS_ACCESS_KEY_ID.

    OpenShift AI stores the credentials as Kubernetes secrets in a protected namespace if you select Secret when you add the variable.

11. In the Cluster storage section, configure the storage for your workbench. Select one of the following options:

    • Create new persistent storage to create storage that is retained after you shut down your workbench. Complete the relevant fields to define the storage:

      1. Enter a name for the cluster storage.
      2. Enter a description for the cluster storage.

      3. Select a storage class for the cluster storage.

         Note

         You cannot change the storage class after you add the cluster storage to the workbench.

      4. Under Persistent storage size, enter a new size in gibibytes or mebibytes.
    • Use existing persistent storage to reuse existing storage and select the storage from the Persistent storage list.

12. Optional: You can add a connection to your workbench. A connection is a resource that contains the configuration parameters needed to connect to a data source or sink, such as an object storage bucket. You can use storage buckets for storing data, models, and pipeline artifacts. You can also use a connection to specify the location of a model that you want to deploy.

    In the Connections section, use an existing connection or create a new connection:

    • Use an existing connection as follows:

      1. Click Attach existing connections.
      2. From the Connection list, select a connection that you previously defined.

    • Create a new connection as follows:

      1. Click Create connection. The Add connection dialog appears.
      2. From the Connection type drop-down list, select the type of connection. The Connection details section appears.

      3. If you selected S3 compatible object storage in the preceding step, configure the connection details:

         1. In the Connection name field, enter a unique name for the connection.
         2. Optional: In the Description field, enter a description for the connection.
         3. In the Access key field, enter the access key ID for the S3-compatible object storage provider.
         4. In the Secret key field, enter the secret access key for the S3-compatible object storage account that you specified.
         5. In the Endpoint field, enter the endpoint of your S3-compatible object storage bucket.
         6. In the Region field, enter the default region of your S3-compatible object storage account.
         7. In the Bucket field, enter the name of your S3-compatible object storage bucket.
         8. Click Create.

      4. If you selected URI in the preceding step, configure the connection details:

         1. In the Connection name field, enter a unique name for the connection.
         2. Optional: In the Description field, enter a description for the connection.
         3. In the URI field, enter the Uniform Resource Identifier (URI).
         4. Click Create.
13. Click Create workbench.

Procedure

1. In your code-server window, from the Activity Bar, select the menu icon ( ) File Open File.
2. In the Open File dialog, click the Show Local button.

3. Locate and select the notebook file and then click Open.

   The file is displayed in the code-server window.

4. Save the file and then push the changes to your repository.

Procedure

1. Copy the HTTPS URL for the Git repository.

   • In GitHub, click ⤓ Code HTTPS and then click the Copy URL to clipboard icon.
   • In GitLab, click Code and then click the Copy URL icon under Clone with HTTPS.
2. In your code-server window, from the Activity Bar, select the menu icon ( ) View Command Palette.
3. In the Command Palette, enter Git: Clone, and then select Git: Clone from the list.
4. Paste the HTTPS URL of the repository that contains your notebook file, and then press Enter.
5. If prompted, enter your username and password for the Git repository.
6. Select a folder to clone the repository into, and then click OK.
7. When the repository is cloned, a dialog appears asking if you want to open the cloned repository. Click Open in the dialog.

Procedure

1. Copy the HTTPS URL for the Git repository.

   • In GitHub, click ⤓ Code HTTPS and then click the Copy URL to clipboard icon.
   • In GitLab, click Code and then click the Copy URL icon under Clone with HTTPS.
2. In your code-server window, from the Activity Bar, select the menu icon ( ) Terminal New Terminal to open a terminal window.

3. Enter the git clone command:

   git clone <git-clone-URL>

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   Replace <git-clone-URL> with the HTTPS URL, for example:

   $ git clone https://github.com/example/myrepo.git
   Cloning into myrepo...
   remote: Enumerating objects: 11, done.
   remote: Counting objects: 100% (11/11), done.
   remote: Compressing objects: 100% (10/10), done.
   remote: Total 2821 (delta 1), reused 5 (delta 1), pack-reused 2810
   Receiving objects: 100% (2821/2821), 39.17 MiB | 23.89 MiB/s, done.
   Resolving deltas: 100% (1416/1416), done.

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Procedure

1. In your code-server window, from the Activity Bar, click the Source Control icon ( ).
2. Click the Views and More Actions button (…), and then select Pull.

Procedure

1. In your code-server window, from the Activity Bar, select the menu icon ( ) File Save All to save any unsaved changes.
2. Click the Source Control icon ( ) to open the Source Control pane.
3. Confirm that your changed files appear under Changes.

4. Next to the Changes heading, click the Stage All Changes button (+).

   The staged files move to the Staged Changes section.

5. In the Message field, enter a brief description of the changes you made.
6. Next to the Commit button, click the More Actions…​ button, and then click Commit & Sync.
7. If prompted, enter your Git credentials and click OK.

Procedure

1. In your code-server window, from the Activity Bar, select the menu icon ( ) Terminal New Terminal to open a terminal window.

2. Enter the pip list command.

   pip list

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Procedure

1. In your code-server window, from the Activity Bar, select the menu icon ( ) File New Text File to create a new text file.

2. Add the packages to install to the text file.

   altair

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   You can specify the exact version to install by using the == (equal to) operator, for example:

   altair==4.1.0

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   Note

   Red Hat recommends specifying exact package versions to enhance the stability of your workbench over time. New package versions can introduce undesirable or unexpected changes in your environment’s behavior.

   To install multiple packages at the same time, place each package on a separate line.

3. Save the text file as requirements.txt.
4. From the Activity Bar, select the menu icon ( ) Terminal New Terminal to open a terminal window.

5. Install the packages in requirements.txt to your server by using the following command:

   pip install -r requirements.txt

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   Important

   The pip install command installs the package on your workbench. However, you must run the import statement to use the package in your code.

   import altair

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