andrespp/jupyterlab repository overview

⁠Docker JupyterLab

⁠Introduction

DockerFile for JupyterLab⁠.

Curent version is 1.2.0 (Changelog⁠)

⁠Resources

⁠Python libraries

This image uses miniconda3⁠ as the default Python distribution, and adds a few packages to it, most of which used in Data Science projects, like: numpy, pandas, matplotlib, seaborn and more.

The additional packages installed on this image are listed on the file (requirements.txt⁠)

⁠JupyterLab Extensions

This image also includes the 3rd-party extensions listed below:

• jupyterlab-drawio⁠: a JupyterLab extension for standalone integration of drawio / mxgraph into jupyterlab.
• jupyterlab-git⁠: a JupyterLab extension for version control using Git.
• jupyterlab-github⁠: a JupyterLab extension for accessing GitHub repositories.
• jupyterlab-plotly-extension⁠: A JupyterLab extension for rendering Plotly charts.
• jupyterlab-toc⁠: a Table of Contents extension for JupyterLab

⁠Quick start

⁠Docker Run

$ docker run --rm -v $(pwd):/opt/app/data -p 8888:8888 andrespp/jupyterlab

⁠Docker compose

• docker-compose.yml

version: '3.1'

services:
  jupyterlab:
    image: andrespp/jupyterlab
    ports:
      - "8888:8888"
    volumes:
      - ./data:/opt/app/data

• Bring compose up

$ docker-compose up

⁠Install

In order to run the container as if the application was installed locally, download the jlab script to a directory in you $PATH, for example:

$ sudo curl -fsSL https://raw.githubusercontent.com/andrespp/docker-jupyterlab/master/jlab \
       -o /usr/local/bin/jlab
$ sudo chmod +x /usr/local/bin/jlab

Then you'll be able to run JupyterLab in the current directory simply by calling jlab:

$ jlab

⁠Environment Variables

This image uses several environment variables in order to control its behavior, and some of them may be required:

⁠Issues

If you have any problems with or questions about this image, please contact me through a GitHub issue⁠.