Choose your workspace

When launching JupyterLab, we are presented with a web-based interface for Jupyter Notebooks. Depending on which type of notebook we pick, a corresponding kernel will be launched.

When comparing which environment to use we must consider each service’s limitations. For example, if we are using the pandas library with Python, as a regular user the RAM limit is 2 GB. Even as a power user, we would be limited to 20 GB of RAM. If dealing with larger computations, it would make sense to use Spark which offers 1.5 TB that is shared with all notebook instances.

By default, Tensorflow recipe work in a GPU cluster and Python runs within a CPU cluster.

Create a new notebook

In the Adobe Experience Platform UI, select Data Science in the top menu to take you to the Data Science Workspace. From this page, select JupyterLab to open the JupyterLab launcher. You should see a page similar to this.

In our tutorial, we will be using Python 3 in the Jupyter Notebook to show how to access and explore the data. In the Launcher page, there are sample notebooks provided. We will be using the Retail Sales recipe for Python 3.

The Retail Sales recipe is a standalone example which uses the same Retail Sales dataset to show how data can be explored and visualized in Jupyter Notebook. Additionally, the notebook goes further in depth with training and verification. More information about this specific notebook can be found in this walkthrough.