Docker for Data Science

Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server

  • Joshua Cook

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Joshua Cook
    Pages 1-27
  3. Joshua Cook
    Pages 29-47
  4. Joshua Cook
    Pages 49-70
  5. Joshua Cook
    Pages 71-79
  6. Joshua Cook
    Pages 81-101
  7. Joshua Cook
    Pages 103-118
  8. Joshua Cook
    Pages 119-135
  9. Joshua Cook
    Pages 137-178
  10. Joshua Cook
    Pages 179-211
  11. Joshua Cook
    Pages 213-251
  12. Back Matter
    Pages 253-257

About this book

Introduction

Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller.

It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. 

As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies—Python, Jupyter, Postgres—as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms.
 
What  You'll Learn:
  • Master interactive development using the Jupyter platform
  • Run and build Docker containers from scratch and from publicly available open-source images
  • Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type
  • Deploy a multi-service data science application across a cloud-based system

Keywords

Juypter Docker Docker Engine Docker File Juypter Docker Stacks Docker Machine Kaggle Python

Authors and affiliations

  • Joshua Cook
    • 1
  1. 1.Santa MonicaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4842-3012-1
  • Copyright Information Joshua Cook 2017
  • Publisher Name Apress, Berkeley, CA
  • eBook Packages Professional and Applied Computing
  • Print ISBN 978-1-4842-3011-4
  • Online ISBN 978-1-4842-3012-1
  • About this book