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Setting Up a Data Lab Environment - Part 3 - Bashing and composing

We used two scripts initialize.sh and _docker-compose.yml_to help us serve Jupyter notebooks from AWS in the part 2 of this series.

Now, let’s explain how these two scripts work.

Bashing First, the bash script - initialise.sh.

Bash scripts are codes that can be run in the terminal shell in a Unix/Linux environment. initialise.sh helps us install, set the necessary permissions and create some folders.

We first update the environment we are in. sudo is used to allow us to undertake updates/installation as an admin. We also install tree to allow us to visualise folders as a tree.

# Update
sudo apt-get update

# Install tree
sudo apt install tree

Next, we install Docker.

# Download and install docker-compose
sudo curl -L https://github.com/docker/compose/releases/download/1.15.0/docker-compose-`uname -s`-`uname -m` > docker-compose
sudo mv docker-compose /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose

And create some directories.

mkdir docker
mkdir docker/jupyter
mkdir notebook

And restart.

sudo reboot


Next docker-compose.yml. This barely scratches the surface of what Docker can do. But it’s good to start simple.

version: '3'
    image: jupyter/tensorflow-notebook
        - "8888:8888"
        - .:/home/jovyan/work

What we are doing here is to ask Docker to start a container named ‘jupyterone’, using the image ‘jupyter/tensorflow-notebook’ pulled from Docker Hub. We then connect the 8888 port in the container to the AWS EC2 instance’s 8888 port. The current folder we are in is also mapped to the folder ‘/home/jovyan/work’ in the container.

And that’s it. Pretty simple isn’t it?


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Listing NFTs
Extracting and Processing Wikidata datasets
Extracting and Processing Google Trends data
Extracting and Processing Reddit datasets from PushShift
Extracting and Processing GDELT GKG datasets from BigQuery
Some notes relating to Machine Learning
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Using CCapture.js library with p5.js and three.js
Introduction to PoseNet with three.js
Topic Modelling
Three.js Series - Manipulating vertices in three.js
Three.js Series - Music and three.js
Three.js Series - Simple primer on three.js
HTML Scraping 101
(Almost) The Simplest Server Ever
Tweening in p5.js
Logistic Regression Classification in plain ole Javascript
Introduction to Machine Learning Right Inside the Browser
Nature and Math - Particle Swarm Optimisation
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The Nature of Code Ported to Three.js
Primer on Generative Art in Blender
How normal are you? Checking distributional assumptions.
Monte Carlo Simulation of Value at Risk in Python
Measuring Expected Shortfall in Python
Style Transfer X Generative Art
Measuring Market Risk in Python
Simple charts | crossfilter.js and dc.js
d3.js vs. p5.js for visualisation
Portfolio Optimisation with Tensorflow and D3 Dashboard
Setting Up a Data Lab Environment - Part 6
Setting Up a Data Lab Environment - Part 5
Setting Up a Data Lab Environment - Part 4
Setting Up a Data Lab Environment - Part 3
Setting Up a Data Lab Environment - Part 2
Setting Up a Data Lab Environment - Part 1
Generating a Strange Attractor in three.js
(Almost) All the Most Common Machine Learning Algorithms in Javascript
3 Days of Hand Coding Visualisations - Day 3
3 Days of Hand Coding Visualisations - Day 2
3 Days of Hand Coding Visualisations - Day 1
3 Days of Hand Coding Visualisations - Introduction