#JUPYTERLAB KUBERNETES HOW TO#

The challenge with starting Jupyter on a remote machine is accessing the UI. Or maybe it’s simply WFH during Covid that’s caused you to work remotely with machines that you usually work directly on. That dev server will be hosted in a lab somewhere that you never physically see. As your experiments scale, you may run into eitherĮither way, the solution will probably be for you to get a beefy dev server from your DevOps/IT team. Maybe you’ve heard the saying that “data science is anything that causes Excel to crash” The next stage after that is anything that doesn’t fit on a laptop. Model 2: running JupyterLab on a remote server where your datasets live

#JUPYTERLAB KUBERNETES HOW TO#

Get fancy: This great post by Jacob Tomlinson walks through how to launch JupyterLab automatically at computer startup. Further reading about starting JupyterLab is here. The server will run until you shut it down either manually (with CTRL-C) or by closing that terminal window. That command will launch a browser window automatically, but every time you navigate to you’ll get your lab UI. Run JupyterLab: from your Mac terminal - Step 1: Create a new “jupyterlab” conda environment conda create -n jupyterlab ipython jupyterlab python nodejs - Step 2: Switch into that new environment source activate jupyterlab - Step 3: Start JupyterLab jupyter lab It’s an extremely easy way to install all of the interesting, new libraries you’ll want to experiment with. Conda enables you to easily manage packages and environments so you can work with different combinations of libraries on your laptop (or another machine). Run JupyterLab within its own conda environment directly from your mac terminal: JupyterLab provides a richer dev experience than the traditional single-pane “tree” view.

Jupyterlab kubernetes