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 doig 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:
- SSH into the remote machine ; start jupyer lab on the remote server using:
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:
Keep the port number and token handy for now, because using this URL will not currently work. We must move on to step 2
- On your local machine, youll 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).
- We’re done now. Almost. Go to a brower on your local machine and try the URL:
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 amachine: installing an ipython kernel for the environment you want to run in. In order to do so, you need to install ipykernel:
Once you have ipykernel, and an anaconda environment you want to run the notebook on; follow these steps:
- activate the environment you want to run a notebook in
- install the environment as an ipython kernel
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 iython installed on them before I tried this– but I am not sure if it is strictly necesarry. The issue I mostly ran into was not activating the target environment before trying to install it as a kernel.