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Red Hat OpenShift AI Self-Managed

Working in your data science IDE

Working in your data science IDE from Red Hat OpenShift AI Self-Managed

Last Updated: 2025-10-

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Abstract

Prepare your data science integrated development environment (IDE) for developing machine

learning models.

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Table of Contents

PREFACE CHAPTER 1. ACCESSING YOUR WORKBENCH IDE CHAPTER 2. WORKING IN JUPYTERLAB 2.1. CREATING AND IMPORTING JUPYTER NOTEBOOKS 2.1.1. Creating a Jupyter notebook 2.1.2. Uploading an existing notebook file to JupyterLab from local storage 2.1.3. Additional resources 2.2. COLLABORATING ON JUPYTER NOTEBOOKS BY USING GIT 2.2.1. Uploading an existing notebook file from a Git repository by using JupyterLab 2.2.2. Uploading an existing notebook file to JupyterLab from a Git repository by using the CLI 2.2.3. Updating your project with changes from a remote Git repository 2.2.4. Pushing project changes to a Git repository 2.3. MANAGING PYTHON PACKAGES 2.3.1. Viewing Python packages installed on your workbench 2.3.2. Installing Python packages on your workbench 2.4. TROUBLESHOOTING COMMON PROBLEMS IN WORKBENCHES FOR USERS CHAPTER 3. WORKING IN CODE-SERVER 3.1. CREATING CODE-SERVER WORKBENCHES 3.1.1. Creating a workbench 3.1.2. Uploading an existing notebook file to code-server from local storage 3.2. COLLABORATING ON WORKBENCHES IN CODE-SERVER BY USING GIT 3.2.1. Uploading an existing notebook file from a Git repository by using code-server 3.2.2. Uploading an existing notebook file to code-server from a Git repository by using the CLI 3.2.3. Updating your project in code-server with changes from a remote Git repository 3.2.4. Pushing project changes in code-server to a Git repository 3.3. MANAGING PYTHON PACKAGES IN CODE-SERVER 3.3.1. Viewing Python packages installed on your code-server workbench 3.3.2. Installing Python packages on your code-server workbench 3.4. INSTALLING EXTENSIONS WITH CODE-SERVER 3 4 5 5 5 5 6 6 6 7 7 8 9 9 9 11 13 13 13 17 18 18 18 19 20 20 21 21 22 Table of Contents

PREFACE In Red Hat OpenShift AI, when you create a workbench, you select a workbench image that includes an integrated development environment (IDE) for developing your machine learning (ML) models. You can use the following data science IDEs for developing ML models with OpenShift AI: JupyterLab code-server RStudio Server (Technology Preview feature)

NOTE

The RStudio workbench images are currently unavailable for disconnected environments. For information about RStudio Server, see the Release Notes. PREFACE

CHAPTER 1. ACCESSING YOUR WORKBENCH IDE To access a workbench IDE, use the link provided in the OpenShift AI interface. Prerequisite You have created a data science project and a workbench. Procedure

1. From the OpenShift AI dashboard, click Data science projects.
2. Click the name of the project that contains the workbench.
3. Click the Workbenches tab.
4. If the status of the workbench is Running, skip to the next step. If the status of the workbench is Stopped, in the Status column for the workbench, click Start. The Status column changes from Stopped to Starting when the workbench server is starting, and then to Running when the workbench has successfully started.
5. Click the open icon ( ) next to the workbench. Verification A new browser window opens for the workbench IDE. Red Hat OpenShift AI Self-Managed 2.25 Working in your data science IDE

2. Locate and select the notebook file and then click Open. The file is displayed in the File Browser. Verification The notebook file is displayed in the File Browser in the left sidebar of the JupyterLab interface. You can open the notebook file in JupyterLab.

2.1.3. Additional resources

Collaborating on Jupyter notebooks by using Git 2.2. COLLABORATING ON JUPYTER NOTEBOOKS BY USING GIT If your files are stored in Git version control, you can clone a Git repository to work with them in JupyterLab. When you are ready, you can push your changes back to the Git repository so that others can review or use your models.

2.2.1. Uploading an existing notebook file from a Git repository by using JupyterLab

You can use the JupyterLab user 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. Read access for the Git repository you want to clone. 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 the JupyterLab interface, click the Git Clone button ( ). You can also click Git → Clone a repository in the menu, or click the Git icon ( ) and click the Clone a repository button. The Clone a repo dialog opens.
3. Enter the HTTPS URL of the repository that contains your notebook file.
4. Click CLONE.
5. If prompted, enter your username and password for the Git repository. Verification Check that the contents of the repository are visible in the file browser in JupyterLab, or run Red Hat OpenShift AI Self-Managed 2.25 Working in your data science IDE

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.

2.2.2. 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.

2.2.3. 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. You have configured the remote Git repository. CHAPTER 2. WORKING IN JUPYTERLAB

Your most recently pushed changes are visible in the remote Git repository. 2.3. MANAGING PYTHON PACKAGES In JupyterLab, you can view the Python packages that are installed on your workbench image and install additional packages.

2.3.1. Viewing Python packages installed on your workbench

You can check which Python packages are installed on your workbench and which version of the package you have by running the pip tool in a notebook cell. Prerequisites Log in to JupyterLab and open a Jupyter notebook. Procedure

1. Enter the following in a new cell in your Jupyter notebook: !pip list
2. Run the cell. Verification The output shows an alphabetical list of all installed Python packages and their versions. For example, if you use the pip list command immediately after creating a workbench that uses the Minimal image, the first packages shown are similar to the following: Package Version

---

aiohttp 3.7. alembic 1.5. appdirs 1.4. argo-workflows 3.6. argon2-cffi 20.1. async-generator 1. async-timeout 3.0. attrdict 2.0. attrs 20.3. backcall 0.2. Additional resources Installing Python packages on your workbench

2.3.2. Installing Python packages on your workbench

You can install Python packages that are not part of the default workbench by adding the package and the version to a requirements.txt file and then running the pip install command in a notebook cell.

NOTE

CHAPTER 2. WORKING IN JUPYTERLAB

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

Red Hat OpenShift AI Self-Managed 2.25 Working in your data science IDE

Problem

You might have run out of storage space on your workbench.

Resolution

Contact your cluster administrator so that they can perform further checks. Red Hat OpenShift AI Self-Managed 2.25 Working in your data science IDE

CHAPTER 3. WORKING IN CODE-SERVER Code-server is a web-based interactive development environment supporting multiple programming languages, including Python, for working with Jupyter notebooks. With the code-server workbench image, you can customize your workbench environment to meet your needs using a variety of extensions to add new languages, themes, debuggers, and connect to additional services. For more information, see code-server in GitHub.

NOTE

Elyra-based pipelines are not available with the code-server workbench image. 3.1. CREATING CODE-SERVER WORKBENCHES You can create a blank Jupyter notebook or import a Jupyter notebook in code-server from several different sources.

3.1.1. Creating a workbench

When you create a workbench, you specify an image (an IDE, packages, and other dependencies). You can also configure connections, cluster storage, and add container storage. Prerequisites You have logged in to Red Hat OpenShift AI. You have created a project. If you created a Simple Storage Service (S3) account outside of Red Hat OpenShift AI and you want to create connections to your existing S3 storage buckets, you have the following credential information for the storage buckets: Endpoint URL Access key Secret key Region Bucket name For more information, see Working with data in an S3-compatible object store. 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. CHAPTER 3. WORKING IN CODE-SERVER

b. 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: a. 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. b. 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 about setting dashboard configuration options, see Customizing the dashboard.

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: a. Enter a name for the cluster storage. CHAPTER 3. WORKING IN CODE-SERVER

b. Enter a description for the cluster storage. c. Select a storage class for the cluster storage.

NOTE

You cannot change the storage class after you add the cluster storage to the workbench. d. For storage classes that support multiple access modes, select an Access mode to define how the volume can be accessed. For more information, see About persistent storage. Only the access modes that have been enabled for the storage class by your cluster and OpenShift AI administrators are visible. e. 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: a. Click Attach existing connections. b. From the Connection list, select a connection that you previously defined. Create a new connection as follows: a. Click Create connection. The Add connection dialog opens. b. From the Connection type drop-down list, select the type of connection. The Connection details section is displayed. c. If you selected S3 compatible object storage in the preceding step, configure the connection details: i. In the Connection name field, enter a unique name for the connection. ii. Optional: In the Description field, enter a description for the connection. iii. In the Access key field, enter the access key ID for the S3-compatible object storage provider. iv. In the Secret key field, enter the secret access key for the S3-compatible object storage account that you specified. v. In the Endpoint field, enter the endpoint of your S3-compatible object storage bucket. vi. In the Region field, enter the default region of your S3-compatible object storage account. Red Hat OpenShift AI Self-Managed 2.25 Working in your data science IDE