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Docker for data science : building scalable and extensible data infrastructure around the Jupyter Notebook Server

Author: Joshua Cook
Publisher: [Place of publication not identified] : Apress, 2017.
Series: For professionals by professionals
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)994638631
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Joshua Cook
ISBN: 9781484230121 1484230124
OCLC Number: 1002185347
Notes: Includes index.
Description: 1 online resource
Contents: At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Introduction; "Big Data"; Recommended Practice for Learning; Set up a New AWS Account; Configure a Key Pair; Create a New Key Pair on AWS; Create a New Security Group; Create a New EC2 Instance; Configure the New EC2 Instance for Using Docker; Infrastructure Limitations on Data; Pull the jupyter/scipy-notebook image; Run the jupyter/scipy-notebook Image; Monitor Memory Usage; What Size Data Set Will Cause a Memory Exception? What Size Dataset Is Too Large to Be Used to Fit Different Kinds of Simple Models?Measuring Scope of Data Capable of Fitting on T2. Micro; Summary; Chapter 2: Docker; Docker Is Not a Virtual Machine; Containerization; A Containerized Application; The Docker Container Ecosystem; The Docker Client; The Host; The Docker Engine; The Docker Image and the Docker Container; The Docker Registry; Docker Compose; Get Docker; Docker for Linux; Installing Docker on an Ubuntu System; Configure Docker Repository; Install from Docker Repository; Manage Docker as a Non-Root User; Docker for Mac. Docker for WindowsDocker Toolbox; Hello, Docker!; Basic Networking in Docker; Summary; Chapter 3: Interactive Programming; Jupyter as Persistent Interactive Computing; How Not to Program Interactively; Setting Up a Minimal Computational Project; Writing the Source Code for the Evaluation of a Bessel Function; Performing Your Calculation Using Docker; Compile Your Source Code; Execute Compiled Binary; How to Program Interactively; Launch IPython Using Docker; Persistence; Jupyter Notebooks; Opinionated Docker Stacks; Security in the Jupyter Notebook Server; Jupyter Demo Stack. Launch the jupyter/demo imageThe Jupyter File System; Port Connections; Port Mappings; Data Persistence in Docker; Attach a Volume; Summary; Chapter 4: The Docker Engine; Examining the Docker Workstation; Hello, World in a Container; Run Echo as a Service; Isolating the Bootstrap Time; A Daemonized Hello World; Summary; Chapter 5: The Dockerfile; Best Practices; Stateless Containers; Single-Concern Containers; Project: A Repo of Docker Images; Prepare for Local Development; Configure GitHub; Building Images Using Dockerfiles; Dockerfile Syntax; Designing the gsl Image. Create the gsl Source DirectoryDefine the gsl Image; Build the gsl Image; FROM gcc; LABEL maintainer=@joshuacook; RUN apt-get update & & apt-get install; Commit Changes to GitHub; The Docker Build Cache; Anaconda; Design the miniconda3 Image; Create the miniconda3 Source Directory; Begin the Image with FROM, ARG, and MAINTAINER; Commit Changes to the Local Repository; Idempotently Run the Build; Commit Changes to the Local Repository; Provision the miniconda3 Image; Run the Build; Commit Changes to the Local Repository; Install Miniconda; Run the Build.
Series Title: For professionals by professionals
Responsibility: Joshua Cook.

Abstract:

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  Read more...

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