Scientific Development Workflows
Here are some general notes on my development workflow. I’m sharing these in part because I recently taught my Jupyter + tmux workflow to a few colleagues and they found it very helpful. I figured some general notes about how I develop might be of general interest, so here they are.
I prefer to develop locally on my MacBook Pro whenever possible. My local NeoVim installation is customized to work best on OS X through iTerm2, so whenever I’m editing on a server, I face both a less optimal editing experience and latency issues. However, I occasionally do have to develop primarily on a server, for example, if running a program takes too many resources or takes too long to run locally on my laptop.
All projects have Github repos, which I keep private until a project is preprinted.
Using Tmux When Developing on a Server
While I could develop locally and use git, rsync, or scp to sync code to a server to run it, I find this tedious and time consuming. I much rather develop directly on the server. After SSH’ing in, I need access to an editing terminal tab, and a shell tab, and often many more tabs to get work done. To emulate this on a server, I use tmux (tmux is short for Terminal Multiplexer). Since often I work on a few (sometimes related) projects at once, I use named tmux sessions:
$ tmux new -s <project_name>
Then I use
C-b c to create new “tabs”,
C-b p to go to the previous tab, and
C-b n to go to the next tab (that’s about 95% of the tmux I use, occasionally I’ll need to do something else, usually it’s kill the current window if its misbehaving, with
Note that tmux will maintain all these tabs even during connection loss. You can intentionally detach a session with
C-b d. Sessions can then be reattached with:
$ tmux attach -t <project_name>
Running Jupyter Lab on a Notebook and Editing with Tmux
In the first tab, I often start a jupyter lab server instance, and then use SSH to tunnel that session to my local browser. I use this to start Jupyter lab on the server:
# activate the relevant conda environment $ jupyter lab --no-browser --port=8890. # a different port per project
Then on your local machine:
$ ssh -Y -N -L localhost:8892:localhost:8892 <servername> & # [I 05:36:08.592 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). # [C 05:36:08.613 LabApp] # To access the notebook, open this file in a browser: # file:///home/vsb/.local/share/jupyter/runtime/nbserver-55159-open.html # Or copy and paste one of these URLs: # http://localhost:8892/?token=a48d356da1967842146cbf9091aa7bd02fa04398eba02e12 $ /Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome --app='http://localhost:8892/?token=<token here>'
Where the token is the string Jupyter lab returns. Calling Google Chrome with
--app=<url> launches a new minimal window (e.g. now browser bar), which is nice as it maximizes your notebook on the screen.
Note that you should be putting all your frequently used servers in
.ssh/config as named entries, so you can just do
ssh servername. Additionally, you should be using key-based authentication (all of this is described in my book).