As previously mentioned, the Jupyter Notebook runs in the client (browser) and connects to a server (either running locally or remotely) to perform necessary calculations in a given language.
The kernels provide the means for establishing this communication, and are effectively what
jupyter (the program) is set up to manage.
Here, we demonstrate why this approach is so valuable.
Assuming you followed the instructions in Installing Anaconda on your local machine, you’ll notice that you establish a connection to the server (running in your Terminal session, likely outputting information with green-highlighted time-stamps) through the “address”
localhost, which references the fact that you are running this server locally.
However, this means that you can follow those instructions on any server, and with some additional commands (which we will review here), have the ability to connect to your Jupyter session from anywhere!1
There are a few situations you might find yourself in, and we attempt to address several common ones here. If there is a scenario that is not covered here that you would like us to write about, please contact us.
Server on Campus
- Provided the machine running Jupyter is one that is publicly accessible. More on that later. ^