David Pugh - Best practices for managing Jupyter-based data science | JupyterCon 2020

Brief Summary This talk covers "best practices" for managing Jupyter-based data science projects using Conda (+Pip). The talk contrasts a "system-wide" Jupyter install where Conda (+Pip) are used to manage a Jupyter installation that is shared across all projects with "project-specific" Jupyter installs where Conda (+Pip) are used to manage Jupyter separately for each project. Outline Outline This talk will cover "best practices" for managing Jupyter-based data science projects using Conda (+Pip). The first half of the talk will discuss the merits of a "system-wide" Jupyter install where Conda (+Pip) are used to manage a Jupyter installation that is shared across all projects. Benefits of a system-wide install of Jupyter are a common set of JupyterLab extensions available for all projects which simplifies UI/UX; no need to frequently re-build JupyterLab; quicker start for prototyping new projects as no need to install Jupyter (+dependencies). Particular focus will be given on how to create project-specific Conda environments with custom kernels allowing users to launch Jupyter Notebooks and Python consoles for each separate project within a common JupyterLab. This part of the talk will also cover the %conda and %pip magic commands and their role in development and prototyping environments in Jupyter Notebooks. The second half of the talk will contrast the system-wide Jupyter install with a "project-specific" Jupyter install where Conda (+Pip) are used to manage separate Jupyter installations for each project. Benefits of this approach are more flexible UI/UX as JupyterLab extensions can customized for each project; ability to have different versions of JupyterLab installed on the same machine allows for experimentation with bleeding edge features; project specific Jupyter install managed with Conda (+Pip) automatically makes a data science project "binder-ready". Examples of project specific JupyterLab installations will be given, including examples of JupyterLab