How to access, download, and analyze data for S3 usage

In this learning path, you will start your Jupyter notebook server and select preferences for S3 usage. You will also learn how to access and download the data you create as well as analyze it, using a variety of skills and tools.

Overview: How to access, download, and analyze data for S3 usage

Red Hat OpenShift Data Science is a managed cloud service for data scientists and developers of intelligent applications. It provides a fully supported environment in which to rapidly develop, train, and test machine learning models in the public cloud before deploying them in production. When you use OpenShift Data Science, you are running in  Red Hat OpenShift Dedicated or Red Hat OpenShift Service on AWS, which simplifies cloud operations.

In this learning path, you will set up options for your Jupyter notebook server.  If you cannot remember how to launch JupyterHub, go back to the Launch JupyterHub learning path.

Select options for your notebook server

When you first gain access to JupyterHub, a configuration screen (Figure 1) gives you the opportunity to select a notebook image and configure the deployment size and environment variables for your data science project.

The JupyterHub options for creating and running a notebook.
Figure 1. JupyterHub offers several options for creating and running a notebook.


You can customize the following options:

  • Notebook image
  • Deployment size
  • Environment variables

The following subsections list which prerequisite you need to choose for this activity.

Notebook image

You can choose from a number of predefined images. When you choose a predefined image, your JupyterLab instance has the associated libraries and packages that you need to do your work.

Available notebook images include:

  • Minimal Python
  • PyTorch
  • Standard Data Science
  • Tensorflow

For this learning path, choose the Standard Data Science notebook image.

Deployment size

You can choose different deployment sizes (resource settings) based on the type of data analysis and machine learning code you are working on. Each deployment size is pre-configured with specific CPU and memory resources.

For this learning path, select the Small deployment size.

Environment variables

The environment variables section is useful for injecting dynamic information that you don't want to save in your notebook.

This learning path does not use any environment variables.

If you are satisfied with your notebook server selections, click the Start Server button to start the notebook server. Once the new notebook server starts, JupyterLab opens and you are ready to experiment.

If this procedure is successful, you have created a new notebook image.