Jupyter Notebooks are a great way to do exploratory data analysis, preparing small scripts, launch workflows on the HPC cluster, and write up tutorials. That said, Jupyter Notebooks are rarely ideal for writing research code. This isn't to say that you should avoid Jupyter Notebooks, but as they say: with great power comes great responsibility.
Jupyter Locally
To use Jupyter locally, it is strongly recommended that you use VS Code (see📝Text Editors) with the Jupyter extension. This is far more streamlined than trying to use Jupyter in the browser.
Jupyter on the Clusters
To use a Jupyter Notebook on the cluster, you have two options.
The first option is to once again use VS Code (see📝Text Editors). You should use the Remote - SSH extension to have VS Code SSH into the remote machine. Everything then works like normal. Make sure you have completed the 😴Removing Tedium section to ensure that VS Code is able to connect.
Princeton Research Computing also has a dedicated Jupyter Lab platform that you can use with the clusters as described in this guide. If you decide to use the Princeton-hosted Jupyter Lab interface, make sure you select the appropriate Anaconda version that matches the module you typically use on the cluster. If you don't see your environment, you can try registering it as described below, where cms is the environment you wish to use:
If using the Jupyter Lab platform on mytiger.princeton.edu, be sure to use "Jupyter on Tiger3 Visualization nodes" to select our dedicated login server.