Chapter 4. Using the Jupyter application Red Hat OpenShift AI provides access to Jupyter as an enabled application for situations where, for example, you do not want users to have their own data science projects or you want to open a notebook that was developed outside of OpenShift AI and has no dependencies on other environments. Note that the preferred way to access Jupyter on OpenShift AI is through a data science project, as described in Creating a workbench . The advantages to using an OpenShift AI data science project and creating a workbench that includes Jupyter, is that your project organizes your data science work in one place and adds functionality such as connections so that you can access data and save your models and pipelines for automating your ML workflow. 4.1. 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 Red Hat OpenShift AI. 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 In the left navigation pane, click Applications Enabled . Locate the Jupyter tile on the Enabled applications page. 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 OpenShift AI. Ask your administrator to add you to the correct group by using Adding users to OpenShift AI 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. Start a notebook server. 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 (?) to a notebook image name to view a list of its included packages. 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 version remains available and supported on the cluster. This gives you time to migrate your work to the latest version of the notebook image. From the Container size list, select a suitable container size for your server. Optional: From the Accelerator list, select an accelerator. 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 . 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. Optional: Check Start server in current tab . 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. 4.2. Creating and importing notebooks You can create a blank notebook or import a notebook in JupyterLab from several different sources. 4.2.1. Creating a new notebook You can create a new Jupyter notebook from an existing notebook container image to access its resources and properties. The Notebook server control panel contains a list of available container images that you can run as a single-user notebook server. Prerequisites Ensure that you have logged in to Red Hat OpenShift AI. Ensure that you have launched your notebook server and logged in to JupyterLab. The notebook image exists in a registry, image stream, and is accessible. Procedure Click File New Notebook . If prompted, select a kernel for your 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 from local storage into JupyterLab to continue work, or adapt a project for a new use case. Prerequisites Credentials for logging in to JupyterLab. A launched and running Jupyter notebook server. A notebook file exists in your local storage. Procedure In the File Browser in the left sidebar of the JupyterLab interface, click Upload Files ( ). Locate and select the notebook file and then click Open . The file is displayed in the File Browser . Verification The notebook file appears in the File Browser in the left sidebar of the JupyterLab interface. You can open the notebook file in JupyterLab. 4.2.3. Additional resources Collaborating on notebooks using Git 4.3. Collaborating on notebooks by using Git If your notebooks or other 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. 4.3.1. 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 A launched and running Jupyter notebook server. Read access for the Git repository you want to clone. Procedure 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 . 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 appears. Enter the HTTPS URL of the repository that contains your notebook. Click CLONE . 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 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 A launched and running Jupyter notebook server. Procedure 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 . In JupyterLab, click File New Terminal to open a terminal window. Enter the git clone command: Replace git-clone-URL> with the HTTPS URL, for example: 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 configured the remote Git repository. You have imported the Git repository into JupyterLab, and the contents of the repository are visible in the file browser in JupyterLab. You have permissions to pull files from the remote Git repository to your local repository. You have credentials for logging in to Jupyter. You have a launched and running Jupyter server. Procedure In the JupyterLab interface, click the Git button ( ). 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 notebook in the JupyterLab interface. You have added the relevant Git repository to your notebook server. You have permission to push changes to the relevant Git repository. You have installed the Git version control extension. Procedure Click File Save All to save any unsaved changes. Click the Git icon ( ) to open the Git pane in the JupyterLab interface. 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. Commit your changes. Ensure that all files under Changed have a blue checkmark beside them. In the Summary field, enter a brief description of the changes you made. Click Commit . Click Git Push to Remote to push your changes to the remote repository. When prompted, enter your Git credentials and click OK . Verification Your most recently pushed changes are visible in the remote Git repository. 4.4. Managing Python packages In JupyterLab, you can view the Python packages that are installed on your notebook image and install additional packages. 4.4.1. Viewing Python packages installed on your notebook server You can check which Python packages are installed on your notebook server 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 notebook. Procedure Enter the following in a new cell in your notebook: !pip list 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 notebook server that uses the Minimal image, the first packages shown are similar to the following: Additional resources Installing Python packages on your notebook server 4.4.2. Installing Python packages on your notebook server You can install Python packages that are not part of the default notebook server by adding the package and the version to a requirements.txt file and then running the pip install command in a notebook cell. 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 notebooks. Prerequisites Log in to JupyterLab and open a notebook. Procedure 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 . Rename the text file to requirements.txt . Right-click the name of the file and then click Rename Text . The Rename File dialog opens. Enter requirements.txt in the New Name field and then click Rename . Add the packages to install to the requirements.txt file. You can specify the exact version to install by using the == (equal to) operator, for example: Note Red Hat recommends specifying exact package versions to enhance the stability of your notebook server 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. Install the packages in requirements.txt to your server by using a notebook cell. Create a new cell in your notebook and enter the following command: Run the cell by pressing Shift and Enter. Important The pip install command installs the package on your notebook server. However, you must run the import statement in a code cell to use the package in your code. Verification Confirm that the packages in the requirements.txt file appear in the list of packages installed on the notebook server. See Viewing Python packages installed on your notebook server for details.

Commands:
git clone <git-clone-URL>
[1234567890@jupyter-nb-jdoe ~]USD 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.
!pip list
Package Version --------------------------------- ---------- aiohttp 3.7.3 alembic 1.5.2 appdirs 1.4.4 argo-workflows 3.6.1 argon2-cffi 20.1.0 async-generator 1.10 async-timeout 3.0.1 attrdict 2.0.1 attrs 20.3.0 backcall 0.2.0
altair
altair==4.1.0
!pip install -r requirements.txt
import altair