Home > Workload Solutions > Container Platforms > Red Hat OpenShift Container Platform > Archive > Machine Learning Using Red Hat OpenShift Container Platform > Launching a Jupyter Notebook
Jupyter is a collaborative tool that data scientists use to develop and execute code, documentation, and visualization in their ML model development process. For more information, see the Jupyter website.
To create and manage Notebook servers in your Kubeflow deployment:
A Notebook management window opens, as shown in the following figure:
A menu shows the options that are available for configuring the notebook server:
A pod is deployed using the TensorFlow container image that you specified. To verify the pod, open the Notebook section of the Kubeflow dashboard, as shown in the following figure:
A Notebook server is created using the options you selected, as shown in the following figure:
The Jupyter notebook opens, as shown in the following figure:
An empty code cell opens in which you can run Python code to display the TensorFlow version that is installed in the notebook. The following figure shows the expected version, v1.13.1: