Hi @KevinB. Clearly, it is a good pratice to use tmux to deal with running a Jupyter Notebook on an online GPU. I found in the forum a short howto guide and a post about “Restoring Jupyter client’s GUI (scores and graphs…etc) after client disconnects for long time” (and a “tmux shortcuts & cheatsheet” page).
About GCP: as the notebook server is launched with the instance in GCP, I’m not sure if tmux is needed (a ssh disconnection will not stop the notebook server) but it is good practice. I do the following:
-
SSH connection to my fastai instance:
gcloud compute ssh --zone=MY-ZONE-INSTANCE jupyter@MY-FASTAI-INSTANCE -
Launch of my tmux session:
tmux new -s fastai -
Creation of 2 vertical panes:
ctrl b + % -
Launch of the notebook server on port 8081 (as the port 8080 is already used):
jupyter notebook --NotebookApp.iopub_msg_rate_limit=1e7 --NotebookApp.iopub_data_rate_limit=1e10 --port 8081 --no-browser -
Detach my tmux session
ctrl b + d -
Stop my ssh connection:
exit -
SSH connection again to my fastai instance but now with association between the port 8081 of the jupyter notebook server on my GCP fastai instance and my local port 8080 on my computer
gcloud compute ssh --zone=MY-ZONE-INSTANCE jupyter@MY-FASTAI-INSTANCE -- -L 8080:localhost:8081
That’s it. I can run my notebooks from my GCP fastai instance at http://localhost:8080/tree on a web browser. If my ssh connection stops, my Jupyter server is still running in the tmux session on GCP. I have only to redo the step 7 above and I get back my notebooks which were not stopped by the ssh disconnection.