Part 1: How to run Jupyter Lab on a remote computer

Now that I am fully immersed in the hybrid workflow, I find myself juggling different computers more and more. For example, I rarely go into the lab on Mondays– but might find myself wanting to do some higher powered computing in a notebook. Symbolic manipulation is, for example, at least two times faster on my iMac in the lab than my laptop. It turns out that running a jupyter notebook on a remote server is super easy, allowing you to use a remote computer’s superior processing power with the convenience of a portable local machine. Honestly, it makes me wonder if I should ever be hosting notebooks on my laptop anymore…

Before doing this, make sure you can set up a an ssh tunnel into the remote machine from your local one. Anyway, here is what you do:

  1. SSH into the remote machine ; start jupyter lab on the remote server using:
jupyter lab --no-browser

If everything ent according to plan, the terminal will give you a URL to access the jupyter lab you just started. Look for something like:

http://localhost:<PORT NUMBER>lab?token=<TOKEN>

Keep the port number and token handy for now, because using this URL will not currently work. We must move on to step 2

  1. On your local machine, you’ll need to open up an ssh tunnel to port <PORT NUMEBR> on the remote machine with the following command:

Note that you might need to put in a password depending on your ssh settings for both machines. (if you have a server listed in your ~/.ssh/config, you can just use the server name rather than the whole <REMOTE USERNAME>@<REMOTE MACHINE> deal).

  1. We’re done now. Almost. Go to a browser on your local machine and try the URL:
http://localhost:<PORT NUMBER>

Did it work? Probably not, because it is going to prompt you to enter the <TOKEN> from earlier. Once you do that, you should be home free.

Part 2: Is this useful?

Well, if you are trying to do some prototyping that requires any cells that would be sped up by the remote machine then I suppose the answer is yes. Though, if none of your cells take longer than a handful of seconds, this might be offset by the extra lag time of using a remote machine. But, what about using this for larger runs of code like a large suite of simulations using MPI. You can actually manage these kinds of runs with a notebook without too much trouble, the major hurdle here is making sure that the notebook uses the right environment. This can be done with proper configuring of your remote machine: installing an ipython kernel for the environment you want to run in. In order to do so, you need to install ipykernel:

conda install -c anaconda ipykernel

Once you have ipykernel, and an anaconda environment you want to run the notebook on; follow these steps:

  1. activate the environment you want to run a notebook in
conda activate <ENV NAME>
  1. install the environment as an ipython kernel
python -m ipykernel install --user --name=<ENV NAME>

Now, when you go to the select kernel dropdown in jupyter lab, you should be an option for the environment you installed.

Troubleshooting note: I made sure that my environment had both jupyter and ipython installed on them before I tried this– but I am not sure if it is strictly necessary. The issue I mostly ran into was not activating the target environment before trying to install it as a kernel.