Jupyter Notebooks have seen a rise in popularity with the data science community over the last few years. This is in part because of the following reasons:
- Language of choice — Python, R, Julia, and Scala can all be used in a notebook.
- Sharing — A notebook is just a JSON file. This makes sharing them very easy.
- Interactivity — Notebooks can produce HTML, images, LaTeX and other custom outputs which help to provide an interactive experience when exploring data.
- Self Documenting — Within a notebook, you can mix in Markdown formatted text with code and visuals to describe what the notebook is doing.
Introducing PHC Notebooks
The LifeOmic Precision Health Cloud (PHC) allows one to analyze data in easy to customize ways using features like the Subject Viewer and Insights. There are times when it is just easier to have direct access to the data in order to explore it. This is why we have released a new feature allowing our users the ability to run notebooks in the PHC. With this feature, users of the PHC get the following:
- The JupyterLab interface for running notebooks.
- A Data Science notebook environment that contains popular Python, R, and Julia packages.
- A Deep Learning notebook environment that provides GPU resources and deep learning frameworks like TensorFlow and PyTorch.
- The PHC SDK for Python that allows one to interact with data from the PHC within a notebook.
- A dedicated work space for storing data and notebook files. The same security principles applied to the PHC are utilized with notebooks. This provides a secure environment for working with sensitive data.
Sharing made easy
To help make sharing notebooks easier, the PHC can render a notebook file’s contents and outputs. You may be working with others that are not as technical or familiar with notebooks. To share your results, you can store a notebook file in a PHC project. You can then share a direct link to the notebook file with others that have been granted access to the project. The direct link will show the rendered notebook.