Accessing the OpenShift Console

1. Open OpenShift Console

   

   → Select openshift as your login option.

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2. Click “Log in with openshift” to continue.

   

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3. Enter your credentials and click Sign In.

   

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4. Register a new user if you don’t already have one.

   

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5. After logging in, skip the tour or click “Get started” to explore the Developer Console.

   

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6. From the left menu, select “+Add” to view getting started options.

   

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7. Click the grid icon on the top bar to access available OpenShift applications.

   

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8. Select “Red Hat OpenShift AI” from the dropdown menu.

   

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9. Click “Log in with OpenShift” to authenticate your access.

   

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10. Choose “openshift” as the login provider.

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11. Once logged in, navigate to “Data Science Projects.”

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12. Click “Create a project” to start setting up your workspace.

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13. Enter your project name (e.g., arahmani-workshop) and click Create.

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14. Your new project is created.
    Review available sections like Workbenches and Pipelines.

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15. Go to the “Workbenches” tab to start building your development environment.

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16. Click “Create workbench” to start setting up your Jupyter environment.

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17. Select the workbench image from the dropdown list.
    Choose Jupyter | Data Science | CPU | Python 3.12 for this setup.

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18. Specify deployment settings.
    Choose Small container size and ensure Cluster storage is configured with default settings.

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19. Review the configuration summary — including Environment Variables, Storage, and Connections.

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20. Click “Create connection” under the Connections section to add object storage integration.

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21. Select “S3 compatible object storage – v1” as the connection type.
    This enables integration with S3-compatible storage such as MinIO.

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22. Enter the connection details.
    Fill in the following fields:

• Connection name: arahmani-connection
• Access key: minioadmin
• Secret key: (your MinIO secret key)

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23. Complete the connection setup by adding:

• Endpoint: http://minio-api.minio-operator.svc.cluster.local:9000
• Bucket: test

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24. Verify the connection appears under the Connections list.
    Ensure your new S3-compatible connection is active and attached.

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25. Return to the Workbenches tab.
    Your new workbench (arahmani-workbench) should now show status Starting, and then Running once ready.

26. Open your running workbench by clicking on its name.
    This launches your JupyterLab environment inside Red Hat OpenShift AI.

    

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27. In JupyterLab, you can see available options such as:

    • Notebook (Python 3.12)
    • Console (Python 3.12)
    • Elyra Pipeline Editor
    • Terminal and Text File options

    

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28. Open a new terminal and clone the RAFT workshop repository using:

    git clone https://github.com/poc-examples/raft-workshop.git

    

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29. Verify the cloned repository appears in your workspace.
    The folder raft-workshop should include the following files:

    • 1-generate-data.ipynb
    • 2-push-dataset.ipynb
    • 3-finetune.ipynb
    • 4-eval.ipynb
    • config.env
    • setup_raft.sh
    • sample_data/

    

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30. Open and edit the config.env file.
    Update the DATASCIENCE_PROJECT_NAMESPACE field with your project name (e.g., arahmani-workshop).

    Example:

    DATASCIENCE_PROJECT_NAMESPACE = "arahmani-workshop"