I am working in JupyterLab within a Managed Notebook instance, accessed through the Vertex AI workbench, as part of a Google Cloud Project. When the instance is created, there are a number of JupyterLab extensions that are installed by default. In the web GUI, one can click the puzzle piece icon and enable/disable all extensions with a single button click. I currently run a post-startup bash script to manage environments and module installations, and I would like to add to this script whatever commands would turn on the existing extensions. My understanding is that I can do this with
# Status of extensions
jupyter labextension list
# Enable/disable some extension
jupyter labextension enable extensionIdentifierHere
However, when I test the enable/disable command in an instance Terminal window, I receive, for example
[Errno 13] Permission denied: '/opt/conda/etc/jupyter/labconfig/page_config.json'
If I try to run this with sudo, I am asked for a password, but have no idea what that would be, given that I just built the environment and didn't set any password.
Any insights on how to set this up, what the command(s) may be, or how else to approach this, would be appreciated.
Potentially relevant:
Not able to install Jupyterlab extensions on GCP AI Platform Notebooks
Unable to sudo to Deep Learning Image
https://jupyterlab.readthedocs.io/en/stable/user/extensions.html#enabling-and-disabling-extensions
Edit 1: Adding more detail in response to answers and comments (@gogasca, @kiranmathew). My goal is to use ipyleaft-based mapping, through the geemap and earthengine-api python modules, within the notebook. If I create a Managed Notebook instance (service account, Networks shared with me, Enable terminal, all other defaults), launch JupyterLab, open the Terminal from the Launcher, and then run a bash script that creates a venv virtual environment, exposes a custom kernel, and performs the installations, I can use geemap and ipywidgets to visualize and modify (e.g., widget sliders that change map properties) Google Earth Engine assets in a Notebook. If I try to replicate this using a Docker image, it seems to break the connection with ipyleaflet, such that when I start the instance and use a Notebook, I have access to the modules (they can be imported) but can't use ipyleaflet to do the visualization. I thought the issue was that I was not properly enabling the extensions, per the "Error displaying widget: model not found" error, addressed in this, this, this, this, etc. -- hence the title of my post. I tried using and modifying @TylerErickson 's Dockerfile that modifies a Google deep learning container and should handle all of this (here), but both the original and modifications break the ipyleaflet connection when booting the Managed Notebook instance from the Docker image.
Google Managed Notebooks do not support third-party JL extensions. Most of these extensions require a rebuild of the JupyterLab static assets bundle. This requires root access which our Managed Notebooks do not support.
Untangling this limitation would require a significant change to the permission and security model that Managed Notebooks provides. It would also have implications for the supportability of the product itself since a user could effectively break their Managed Notebook by installing something rogue.
I would suggest to use User Managed Notebooks.