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Docker for data science = building s...

Cook, Joshua.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Docker for data science/ by Joshua Cook.
其他題名:	building scalable and extensible data infrastructure around the Jupyter Notebook Server /
作者:	Cook, Joshua.
出版者:	Berkeley, CA :Apress : : 2017.,
面頁冊數:	xxi, 257 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction -- Chapter 2: Docker -- Chapter 3: Interactive Programming -- Chapter 4: Docker Engine -- Chapter 5: The Dockerfile -- Chapter 6: Docker Hub -- Chapter 7: The Opinionated Jupyter Stacks -- Chapter 8: The Data Stores -- Chapter 9: Docker Compose -- Chapter 10: Interactive Development.
Contained By:	Springer eBooks
標題:	Application software - Development. -
電子資源:	http://dx.doi.org/10.1007/978-1-4842-3012-1
ISBN:	9781484230121

Docker for data science = building scalable and extensible data infrastructure around the Jupyter Notebook Server /
Cook, Joshua.

Docker for data sciencebuilding scalable and extensible data infrastructure around the Jupyter Notebook Server /[electronic resource] :by Joshua Cook. - Berkeley, CA :Apress :2017. - xxi, 257 p. :ill., digital ;24 cm.

Chapter 1: Introduction -- Chapter 2: Docker -- Chapter 3: Interactive Programming -- Chapter 4: Docker Engine -- Chapter 5: The Dockerfile -- Chapter 6: Docker Hub -- Chapter 7: The Opinionated Jupyter Stacks -- Chapter 8: The Data Stores -- Chapter 9: Docker Compose -- Chapter 10: Interactive Development.

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.

ISBN: 9781484230121

Standard No.: 10.1007/978-1-4842-3012-1doiSubjects--Topical Terms:

539563
Application software
--Development.

LC Class. No.: QA76.76.A65 / C665 2017

Dewey Class. No.: 005.1

Docker for data science = building scalable and extensible data infrastructure around the Jupyter Notebook Server /
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505 0 $a Chapter 1: Introduction -- Chapter 2: Docker -- Chapter 3: Interactive Programming -- Chapter 4: Docker Engine -- Chapter 5: The Dockerfile -- Chapter 6: Docker Hub -- Chapter 7: The Opinionated Jupyter Stacks -- Chapter 8: The Data Stores -- Chapter 9: Docker Compose -- Chapter 10: Interactive Development.
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