starting-a-jupyter-notebook-server.adoc

Starting a Jupyter notebook server

Jupyter is based on a server-client architecture. The Jupyter notebook server runs in a container on the Red Hat OpenShift cluster. The client is the JupyterLab interface that opens in your web browser on your local computer. However, all of the commands that you enter in JupyterLab are executed by the notebook server. This architecture allows you to interact through your local computer in a browser environment, while all processing occurs on the cluster. The cluster provides the benefits of larger available resources and security because the data being processed never leaves the cluster.

From the Jupyter application tile, you can start a Jupyter notebook server. If you require extra power for use with large datasets, you can assign accelerators to your notebook server to optimize performance.

Prerequisites

• You are logged in to {productname-long}.

• You are starting Jupyter for the first time or you stopped your notebook server.

• You know the names and values you want to use for any environment variables in your notebook server environment, for example, AWS_SECRET_ACCESS_KEY.

• If you want to work with a large data set, work with your administrator to proactively increase the storage capacity of your notebook server. If applicable, also consider assigning accelerators to your notebook server.

Procedure

1. In the left navigation pane, click Applications → Enabled.

2. Locate the Jupyter tile on the Enabled applications page.

3. Click Launch application.

   If you see an Access permission needed message, you are not in the default user group or the default administrator group for {productname-short}. Ask your administrator to add you to the correct group by using Adding users to {productname-short} user groups.

   If you have not previously authorized the jupyter-nb-<username> service account to access your account, the Authorize Access page appears prompting you to provide authorization. Inspect the permissions selected by default, and click the Allow selected permissions button.

   If your credentials are accepted, the Notebook server control panel opens displaying the Start a notebook server page.

4. Start a notebook server.

   1. In the Notebook image section, select the notebook image to use for your server.

      Different notebook images have different packages installed by default. Click the help icon (?) next to a notebook image name to view a list of its included packages.

   2. If the notebook image contains multiple versions, select the version of the notebook image from the Versions section.

      Note	When a new version of a notebook 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 notebook image.

   3. From the Container size list, select a suitable container size for your server.

   4. Optional: From the Accelerator list, select an accelerator.

   5. If you selected an accelerator in the preceding step, specify the number of accelerators to use.

      Important

      Using accelerators is only supported with specific notebook images. For GPUs, only the AMD ROCm, PyTorch, TensorFlow, and CUDA notebook images are supported. In addition, you can only specify the number of accelerators required for your notebook server if accelerators are enabled on your cluster. To learn how to enable accelerator support, see Working with accelerators.

   6. 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.

   7. Optional: Check Start server in current tab.

   8. Click Start server.

      The Starting server progress indicator appears. Click Expand event log to view additional information about the server creation process. Depending on the deployment size and resources you requested, starting the server can take up to several minutes. Only click Cancel if you want to cancel the server creation.

      After the server starts, you see one of the following behaviors:

      • If you selected Start server in current tab in the preceding step, the JupyterLab interface opens in the current tab of your web browser.

      • If you did not select Start server in current tab in the preceding step, the Starting server dialog box prompts you to open the server in a new browser tab or in the current browser tab.

Verification

• The JupyterLab interface opens.

Troubleshooting

• If you see the "Unable to load notebook server configuration options" error message, contact your administrator so that they can review the logs associated with your Jupyter pod and determine further details about the problem.