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Red Hat OpenShift AI Self-Managed

Working with connected applications

Connect to applications from Red Hat OpenShift AI Self-Managed

Last Updated: 2025-10-

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Abstract

Learn how to enable access to connected applications, remove unused applications from your

dashboard, and access and use the Jupyter application that is enabled by default.

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Table of Contents

PREFACE CHAPTER 1. VIEWING APPLICATIONS THAT ARE CONNECTED TO OPENSHIFT AI CHAPTER 2. ENABLING APPLICATIONS THAT ARE CONNECTED TO OPENSHIFT AI CHAPTER 3. REMOVING DISABLED APPLICATIONS FROM THE DASHBOARD CHAPTER 4. USING BASIC WORKBENCHES 4.1. STARTING A BASIC WORKBENCH 4.2. CREATING AND IMPORTING JUPYTER NOTEBOOKS 4.2.1. Creating a Jupyter notebook 4.2.2. Uploading an existing notebook file to JupyterLab from local storage 4.2.3. Additional resources 4.3. COLLABORATING ON JUPYTER NOTEBOOKS BY USING GIT 4.3.1. Uploading an existing notebook file from a Git repository by using JupyterLab 4.3.2. Uploading an existing notebook file to JupyterLab from a Git repository by using the CLI 4.3.3. Updating your project with changes from a remote Git repository 4.3.4. Pushing project changes to a Git repository 4.4. MANAGING PYTHON PACKAGES 4.4.1. Viewing Python packages installed on your workbench 4.4.2. Installing Python packages on your workbench 4.5. UPDATING WORKBENCH SETTINGS BY RESTARTING YOUR WORKBENCH 3 4 5 7 8 8 10 10 10 11 11 11 12 12 13 14 14 14 16 Table of Contents

PREFACE You can extend Red Hat OpenShift AI capabilities by connecting to a wide range of open source and third-party applications, such as Starburst and IBM watsonx.ai. You can also remove unused applications from your OpenShift AI dashboard so that you can focus on the applications that you are most likely to use. PREFACE

CHAPTER 1. VIEWING APPLICATIONS THAT ARE CONNECTED TO OPENSHIFT AI You can view the available open source and third-party connected applications from the OpenShift AI dashboard. Prerequisites You have logged in to Red Hat OpenShift AI. Procedure

1. From the OpenShift AI dashboard, select Applications → Explore. The Explore page displays applications that are available for use with OpenShift AI.
2. Click a tile for more information about the application or to access the Enable button. Note: The Enable button is visible only if an application does not require an OpenShift Operator installation.

Verification

You can access the Explore page and click on tiles. Red Hat OpenShift AI Self-Managed 2.25 Working with connected applications

Verification The application that you enabled is displayed on the Enabled page. The API endpoint is displayed on the tile for the application on the Enabled page. Red Hat OpenShift AI Self-Managed 2.25 Working with connected applications

CHAPTER 3. REMOVING DISABLED APPLICATIONS FROM THE DASHBOARD After your administrator has disabled your unused applications, you can manually remove them from the Red Hat OpenShift AI dashboard. Disabling and removing unused applications allows you to focus on the applications that you are most likely to use. Prerequisites You are logged in to Red Hat OpenShift AI. Your administrator has disabled the application that you want to remove, as described in Disabling applications connected to OpenShift AI. Procedure

1. In the OpenShift AI interface, click Applications → Enabled. On the Enabled page, tiles for disabled applications are denoted with a Disabled label.
2. Click Disabled on the tile for the application that you want to remove.
3. Click the link to remove the application tile. Verification The tile for the disabled application is no longer displayed on the Enabled page. CHAPTER 3. REMOVING DISABLED APPLICATIONS FROM THE DASHBOARD

Different workbench images have different packages installed by default. Click the help icon (?) next to a workbench image name to view a list of its included packages. b. If the workbench image contains multiple versions, select the version of the workbench image from the Versions section.

NOTE

When a new version of a workbench image is released, the previous version remains available and supported on the cluster. This gives you time to migrate your work to the latest version of the workbench image. c. From the Container size list, select a suitable container size for your workbench. d. Optional: From the Accelerator list, select an accelerator. e. If you selected an accelerator in the preceding step, specify the number of accelerators to use.

IMPORTANT

Using accelerators is only supported with specific workbench images. For GPUs, only the AMD ROCm, PyTorch, TensorFlow, and CUDA workbench images are supported. In addition, you can only specify the number of accelerators required for your workbench if accelerators are enabled on your cluster. To learn how to enable accelerator support, see Working with accelerators. f. Optional: Select and specify values for any new Environment variables. The interface stores these variables so that you only need to enter them once. Example variable names for common environment variables are automatically provided for frequently integrated environments and frameworks, such as Amazon Web Services (AWS).

IMPORTANT

Select the Secret checkbox for variables with sensitive values that must remain private, such as passwords. g. Optional: Check Start workbench in current tab. h. Click Start workbench. The Workbench status progress indicator is displayed. Click the Events log tab to view additional information about the workbench creation process. Depending on the deployment size and resources you requested, starting the workbench can take up to several minutes. Only click Cancel if you want to cancel the workbench creation. After the server starts, you see one of the following behaviors: If you selected Start workbench in current tabin the preceding step, the IDE interface opens in the current tab of your web browser. If you did not select Start workbench in current tabin the preceding step, the Workbench status dialog box prompts you to open the server in a new browser tab or in the current browser tab. CHAPTER 4. USING BASIC WORKBENCHES

Verification The IDE interface opens. Troubleshooting If you see the "Unable to load workbench configuration options" error message, contact your administrator so that they can review the logs associated with your workbench pod and determine further details about the problem. 4.2. CREATING AND IMPORTING JUPYTER NOTEBOOKS You can create a blank Jupyter notebook or import a Jupyter notebook in JupyterLab from several different sources.

4.2.1. Creating a Jupyter notebook

You can create a Jupyter notebook from an existing notebook container image to access its resources and properties. The Workbench control panel contains a list of available container images that you can run as a single-user workbench. Prerequisites Ensure that you have logged in to Red Hat OpenShift AI. Ensure that you have launched your workbench and logged in to JupyterLab. The workbench image exists in a registry, image stream, and is accessible. Procedure

1. Click File → New → Notebook.
2. If prompted, select a kernel for your Jupyter notebook from the list. If you want to use a kernel, click Select. If you do not want to use a kernel, click No Kernel. Verification Check that the notebook file is visible in the JupyterLab interface.

4.2.2. Uploading an existing notebook file to JupyterLab from local storage

You can load an existing notebook file from local storage into JupyterLab to continue work, or adapt a project for a new use case. Prerequisites Credentials for logging in to JupyterLab. You have a launched and running workbench based on a JupyterLab image. A notebook file exists in your local storage. Procedure Red Hat OpenShift AI Self-Managed 2.25 Working with connected applications

Verification Check that the contents of the repository are visible in the file browser in JupyterLab, or run the ls command in the terminal to verify that the repository shows as a directory.

4.3.2. Uploading an existing notebook file to JupyterLab from a Git repository by

using the CLI

You can use the command line interface to clone a Git repository into your workspace to continue your work or integrate files from an external project. Prerequisites You have a launched and running workbench based on a JupyterLab image. 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 JupyterLab, click File → New → Terminal to open a terminal window.
3. Enter the git clone command: git clone <git-clone-URL> Replace git-clone-URL> with the HTTPS URL, for example: [1234567890@jupyter-nb-jdoe ~]$ 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. Verification Check that the contents of the repository are visible in the file browser in JupyterLab, or run the ls command in the terminal to verify that the repository shows as a directory.

4.3.3. Updating your project with changes from a remote Git repository

You can pull changes made by other users into your data science project from a remote Git repository. Prerequisites You have a launched and running workbench based on a JupyterLab image. You have credentials for logging in to Jupyter. Red Hat OpenShift AI Self-Managed 2.25 Working with connected applications

You have configured the remote Git repository. You have permissions to pull files from the remote Git repository to your local repository. You have imported the Git repository into JupyterLab, and the contents of the repository are visible in the file browser in JupyterLab. Procedure

1. In the JupyterLab interface, click the Git button ( ).
2. Click the Pull latest changes button ( ). Verification You can view the changes pulled from the remote repository on the History tab in the Git pane.

4.3.4. Pushing project changes to a Git repository

To build and deploy your application in a production environment, upload your work to a remote Git repository. Prerequisites You have opened a Jupyter notebook in the JupyterLab interface. You have added the relevant Git repository to your workbench. You have permission to push changes to the relevant Git repository. You have installed the Git version control extension. Procedure

1. Click File → Save All to save any unsaved changes.
2. Click the Git icon ( ) to open the Git pane in the JupyterLab interface.
3. Confirm that your changed files appear under Changed. If your changed files appear under Untracked, click Git → Simple Staging to enable a simplified Git process.
4. Commit your changes. a. Ensure that all files under Changed have a blue checkmark beside them. b. In the Summary field, enter a brief description of the changes you made. c. Click Commit.
5. Click Git → Push to Remote to push your changes to the remote repository.
6. When prompted, enter your Git credentials and click OK. CHAPTER 4. USING BASIC WORKBENCHES

NOTE

Although you can install packages directly, it is recommended that you use a requirements.txt file so that the packages stated in the file can be easily re-used across different workbenches. Prerequisites Log in to JupyterLab and open a Jupyter notebook. Procedure

1. Create a new text file using one of the following methods: Click + to open a new launcher and then click Text file. Click File → New → Text File.
2. Rename the text file to requirements.txt. a. Right-click the name of the file and then click Rename Text. The Rename File dialog opens. b. Enter requirements.txt in the New Name field and then click Rename.
3. Add the packages to install to the requirements.txt file. altair You can specify the exact version to install by using the == (equal to) operator, for example: altair==4.1.

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.

4. Install the packages in requirements.txt to your server by using a notebook cell. a. Create a new notebook cell and enter the following command: !pip install -r requirements.txt b. Run the cell by pressing Shift and Enter.

IMPORTANT

CHAPTER 4. USING BASIC WORKBENCHES

IMPORTANT

The pip install command installs the package on your workbench. However, you must run the import statement in a code cell to use the package in your code. import altair Verification Confirm that the packages in the requirements.txt file appear in the list of packages installed on the workbench. See Viewing Python packages installed on your workbench for details. 4.5. UPDATING WORKBENCH SETTINGS BY RESTARTING YOUR WORKBENCH You can update the settings on your workbench by stopping and relaunching the workbench. For example, if your server runs out of memory, you can restart the server to make the container size larger. Prerequisites A running workbench. Log in to JupyterLab. Procedure

1. Click File → Hub Control Panel. The Workbench control panel opens.
2. Click the Stop workbench button. The Stop server dialog opens.
3. Click Stop server to confirm your decision. The Start a basic workbench page opens.
4. Update the relevant workbench settings and click Start workbench. Verification The workbench starts and contains your updated settings. Red Hat OpenShift AI Self-Managed 2.25 Working with connected applications