Running Jupyter Notebooks with Visual Studio Code

Jupyter Notebooks offer a convenient way to interactively work on something, but can be a heavy computational workload and are prohibited from running on the head node. You must run notebooks on the worker nodes, as described, in this tutorial.

Notebooks run on the headnode may be terminated without warning

For using Jupyter Notebooks you will need to have:

Visual Studio Code installed on your local machine with Python, Jupyter and Remote SSH extensions enabled.
Installed Jupyter notebook on Arjuna (i.e. via conda or spack)

Instructions

Allocate an interactive worker node with the resources you need, for example:

# Request 1 cpu with 2G of memory for 1 hour
srun -N 1 -n 1 -A <account> -p <partition> --mem=2G -t 01:00:00 --pty bash

# The same, but with a gpu:
srun -N 1 -n 1 -A <account> -p gpu --gres=gpu:1 --mem=2G -t 01:00:00 --pty bash

Replace <account> and <partition> with the appropriate account and partition.

Interactive sessions do count against your fairshare. Generally 2 cores is sufficient for non-parallelized codes. Jupyter can only run on a single machine. Be sure to specify -N 1

On the worker node, launch the notebook server: jupyter notebook --ip $(hostname) --no-browser

After the wall of text, identify the server url containing the ip address of the worker node, something like:

http://c022:8888/?token=a6935b8b842eb4d6916ea36dd76c7bf3d21c8c909f26aab1

where c022 is the hostname of the worker node. Copy it.

Start up Visual Studio Code and choose the server you just started. Do this by opening the command palette (usually Cmd+Shift+P) and search for Jupyter: Select Jupyter Server for Connections. Enter the url from step 2.
Open your notebook and write your code