This is Jupyter Notebook (201904)  running in a Debian 7(GLIBC-2.13) based container.  The default kernel is Python 3.7. New development work should use the Jupyter Notebook (202105) tool.

The Jupyter notebook server is started with a file manager pointed to your home directory.  This makes all files in your home directory accessible and available to any notebook you execute.

To review the list of installed packages issue this command in a notebook cell:

!conda list

To review the environments(kernels) available issue this command in a notebook cell:

!conda env list

To review the list of  packages installed in an environment issue this command in a notebook cell:

!conda list --name envName

There is no shortage of material concerning the benefits of Jupyter notebooks and how to write them.  A set of best practices is provided here.

Tools of interest to those learning about or applying machine learning techniques incorporating Jupyter notebooks include the following:

• Machine Learning for Materials Science: Part 1 - Set of machine learning tutorials focused on querying, visualization and predictive regression of materials properties
• Machine Learning Lab Module - Overview of a materials science machine learning workflow with activities
• Querying Data Repositories - Set of tutorials on how to organize, query and visualize data from online repositories. Covers Materials Project, Wolframalpha, and Citrination.
• Citrine Tools for Materials Informatics - Notebooks focused on "active learning" techniques to accelerate materials discovery through informed experiments

Researchers should cite this work as follows:

• (2022), "Jupyter Notebook (201904)," https://nanohub.org/resources/jupyter60. (DOI: 10.21981/3GXX-A295).

  BibTex | EndNote

1. Jupyter notebooks