Jupyter - bobbae/gcp GitHub Wiki
Jupyter Notebook is based on IPython
Jupyter is an interactive computational environment, in which you can combine code execution, rich text, mathematics, plots and rich media.
The name “Jupyter” is a reference to Galileo, who detailed his discovery of the Moons of Jupiter in his astronomical notebooks.
The name “Jupyter” is also a play on the languages Julia, Python, and R, which are pillars of the modern scientific world.
JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data.
There are many ways to run Jupyter notebooks in GCP.
Vertex AI Notebook, AI Platform, Datalab, Dataproc, and Colab all support Jupyter notebooks.
You could also create a GCE VM and install Jupyter notebook manually or use terraform to provision a AI Platform Jupyter Notebook via Cloud Shell.
You can also start Jupyter notebook servers on Google Kubernetes Engine.
You can create Jupyter Classroom environment with Google Container Engine.
Tensorflow and Jupyter can be used together.
You can use Dataproc Hub to combine Jupiter Notebooks with ML, Spark and Hadoop based data lakes and open source data solutions.
Jupyter tips & tricks
https://www.youtube.com/watch?v=2eCHD6f_phE
Jupyter Kubeflow notebooks
https://www.youtube.com/watch?v=eEsfPL6SvJc
JupyterHub
With JupyterHub you can create a multi-user Hub which spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. JupyterHub allows users to interact with a computing environment through a webpage. As most devices have access to a web browser, JupyterHub makes it is easy to provide and standardize the computing environment for a group of people (e.g., for a class of students or an analytics team).
Helm Chart & Documentation for deploying JupyterHub on Kubernetes.