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Workshop 1.1: Jupyter Notebooks Introduction

Getting up to speed..

Getting up to speed..

What is Interactive Computing?

An Interactive computation is a persistent computer program that runs with a “human in the loop” where the primary mode of steering the program is through the human iteratively writing/running blocks of code and looking at the result.

Reference:https://www.youtube.com/watch?v=YKmJvHjTGAM&list=PL055Epbe6d5aP6Ru42r7hk68GTSaclYgi&index=29

Interactivity as a Protocol

  • Read-Evaluate-Print Loop (REPL):

    • Takes a single user’s inputs

    • Evaluates them

    • Returns the result to the user

  • A two-process model based on a kernel-client infrastructure

    • Kernel: Executes code

    • Client: Reads input and present output

IPython

Reference: https://ipython.readthedocs.io/en/stable/about/history.html

Reference: https://ipython.readthedocs.io/en/stable/interactive/python-ipython-diff.html

Fernando Perez Inspiration

  • Research with open tools for access and collaboration

    • Validated in SciPy India 2010 - Workshop to include students from underprivileged colleges in rural India.

  • Scientific

    • Business of science is to understand nature

    • Science is about opening up the black boxes nature

  • Community!

    • SciPy: Scientists collaborating and building better tools together!

    • Less competitions and more collaboration!

reference: https://www.youtube.com/watch?v=xuNj5paMuow&list=PL055Epbe6d5aP6Ru42r7hk68GTSaclYgi

What are Jupyter Notebooks?

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. The Jupyter notebook combines two components:

  • A web application: a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output.

  • Notebook documents: a representation of all content visible in the web application, including inputs and outputs of the computations, explanatory text, mathematics, images, and rich media representations of objects.

Uses include:

  • Data cleaning and transformation

  • Statistical modeling

  • Data visualization

  • Machine learning, and much more

reference: https://jupyter.org/

What does it look like?

print("Hola Roberto!")
Hola Roberto!
for x in range(5):
    print(x)
0
1
2
3
4

Architecture (Interactive Protocol)

Installing Jupyter Notebooks (Manually)

Prerequisite: Python

While Jupyter runs code in many programming languages, Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the JupyterLab or the classic Jupyter Notebook.

Classic Jupyter Notebook

Using Mamba or Conda

mamba install -c conda-forge notebook
conda install -c conda-forge notebook

Using PIP

pip install notebook

Once Jupyter Notebook is installed, you can run it with the following command:

jupyter notebook

Installing Jupyter Notebooks (Manually)

Prerequisite: Python

While Jupyter runs code in many programming languages, Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the JupyterLab or the classic Jupyter Notebook.

JupyterLab

Using Mamba or Conda

mamba install -c conda-forge jupyterlab
conda install -c conda-forge jupyterlab

Using PIP

pip install jupyterlab

Once JupyterLab is installed, you can run it with the following command:

jupyter-lab

from IPython.display import YouTubeVideo

YouTubeVideo('KcoYnFVw9Xs', width=1000, height=800)

Installing Jupyter Notebooks (Docker CE)

Prerequisite: Docker CE

You just have to install the community edition of Docker, so that you can install JupyterLab from Docker images containing Jupyter applications and interactive computing tools. You can use a docker stack image to do any of the following (and more):

  • Start a personal Jupyter Notebook server in a local Docker container

  • Run JupyterLab servers for a team using JupyterHub

  • Write your own project Dockerfile

You can use ready-to-run Docker images maintained by the Jupyter team:

Running the Jupyter Base Notebook

docker run -p 8888:8888 jupyter/minimal-notebook:3b1f4f5e6cc1
from IPython.display import YouTubeVideo

YouTubeVideo('KVR1_cVlLRE', width=1000, height=800)

Jupyterthon - OTRF Docker Image

docker run -ti --rm -p 8888:8888 otrf/jupyterthon:2021

Jupyter Hub

JupyterHub is a set of processes that together provide a single user Jupyter Notebook server for each person in a group. Three major subsystems are started by the jupyterhub command line program:

  • Hub (Python/Tornado): manages user accounts, authentication, and coordinates Single User Notebook Servers using a Spawner.

  • Proxy: the public facing part of JupyterHub that uses a dynamic proxy to route HTTP requests to the Hub and Single User Notebook Servers. configurable http proxy (node-http-proxy) is the default proxy. Single-User Notebook Server (Python/Tornado): a dedicated, single-user, Jupyter Notebook server is started for each user on the system when the user logs in. The object that starts the single-user notebook servers is called a Spawner.

Reference: https://jupyterhub.readthedocs.io/en/latest/reference/technical-overview.html

Enter Binder Project

  • Released in May, 2016

  • Updated 2.0 on November, 2019

  • The Binder Project is an open community that makes it possible to create shareable, interactive, reproducible environments.

  • The main technical product that the community creates is called BinderHub, and one deployment of a BinderHub exists at mybinder.org.

  • Who is it for?:

    • Researchers, Educators, people analyzing data and people trying to communicate the data analysis to others!!

BinderHub

BinderHub connects several services together to provide on-the-fly creation and registry of Docker images. It utilizes the following tools:

  • A cloud provider such Google Cloud, Microsoft Azure, Amazon EC2, and others

  • Kubernetes to manage resources on the cloud

  • Helm to configure and control Kubernetes

  • Docker to use containers that standardize computing environments

  • A BinderHub UI that users can access to specify Git repos they want built

  • BinderHub to generate Docker images using the URL of a Git repository

  • A Docker registry (such as gcr.io) that hosts container images

  • JupyterHub to deploy temporary containers for users

BinderHub Flow

Let’s Try it!

JupyterLab Desktop App

  • Released in September 2021.

  • Cross-platform standalone application distribution of JupyterLab.

  • Desktop app which bundles a Python environment (bundled conda Python env) with popular Python libraries (Numpy, scipy, pandas, ipywidgets and matplotlib).

  • Based on Electron with a front-end of JupyterLab inside an embedded browser.

from IPython.display import YouTubeVideo

YouTubeVideo('1g1c81YTONo', width=1000, height=800)

Classic Jupyter Notebook

docker run -ti --rm -p 8888:8888 otrf/jupyterthon:2021

JupyterLab

docker run -ti --rm -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 otrf/jupyterthon:2021

VSCode Jupyter Notebook

  • Visual Studio Code

  • Python Extension for Visual Studio Code

  • Python Interpreter: you can find the steps to install it here.

References:

from IPython.display import YouTubeVideo

YouTubeVideo('4LrBMHL69VI', width=1000, height=800)

Break: 5 Minutes