When data science users create notebooks, they need to have some control over the notebook initial state. They would like a consistent starting point for notebooks, and they would like new notebooks to automatically have the packages they need for their data science use cases.  

Requirements:

Considerations/questions:

• Users will keep moving forward with newer versions of components.  Need to determine how long we support the previous version if it introduces breaking changes or is a major release change.

• Should we include other packages, such as  eg. Seaborn, sklearn?
• Need to define specific supported releases
• list of supported versions for each package & software versions; make available as help content
• list of available images is fixed; 
• need to validate list of packages vs. what is most needed; check w/ Sophie's list ; can we find out what packages users are installing; ability to request a new package

Most popular python libraries: 1) numpy; 2) pandas; 3) matplotlib; 4) sklearn (scikit-learn); 5) os; 6) seaborn; 7) scipy

https://blog.jetbrains.com/datalore/2020/12/17/we-downloaded-10-000-000-jupyter-notebooks-from-github-this-is-what-we-learned/ 

• get metrics on what packages users are installing; 
• might need to notify users to provide guidance on resetting NB server
• separate epic for NB server lifecycle 
• Need to determine timing for incorporating latest released versions

latest version sheet here

• Need to provide specific version for Cuda in name? See supported version sheet linked above
• 3/29/21: We're now planning to only have 1 image each for Tensorflow and PyTorch. The images will work for both CPU & GPU.