This article discusses two approaches for managing JupyterLab-based data science projects using Conda (+pip): a “system-wide” approach where Conda (+pip) are used to manage a single JupyterLab installation that is shared across all projects, and a “project-based” approach where Conda (+pip) are used to manage separate JupyterLab installations for each project. After describing the two approaches I will walk through some examples and discuss the relevant tradeoffs.

“System-wide” JupyterLab install

With a “system-wide” approach to managing JupyterLab, Conda (+pip) are used to manage a JupyterLab installation that is shared across all or your data science projects. There are several benefits to a “system-wide” approach.

• Common set of JupyterLab extensions simplifies user interface (UI) and user experience (UX).
• Allows for quicker start of new projects as no need to install (and build!) JupyterLab for every project.
• Easy low-level configuration of JupyterLab via files inside the ~/.jupyter directory in your user home directory.

#conda #towards-data-science #data-science #machine-learning #jupyter-notebook