東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Docker for data science = building s...

Cook, Joshua.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Docker for data science/ by Joshua Cook.
Reminder of title:	building scalable and extensible data infrastructure around the Jupyter Notebook Server /
Author:	Cook, Joshua.
Published:	Berkeley, CA :Apress : : 2017.,
Description:	xxi, 257 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Application software - Development. -
Online resource:	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 /
LDR:02752nmm a2200289 a 4500 001 2106526
003 DE-He213
005 20180320165652.0
006 m d
007 cr nn 008maaau
008 180417s2017 cau s 0 eng d
020 $a 9781484230121 $q (electronic bk.)
020 $a 9781484230114 $q (paper)
024 7 $a 10.1007/978-1-4842-3012-1 $2 doi
035 $a 978-1-4842-3012-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.A65 $b C665 2017
082 0 4 $a 005.1 $2 23
090 $a QA76.76.A65 $b C771 2017
100 1 $a Cook, Joshua. $3 3252466
245 1 0 $a Docker for data science $h [electronic resource] : $b building scalable and extensible data infrastructure around the Jupyter Notebook Server / $c by Joshua Cook.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2017.
300 $a xxi, 257 p. : $b ill., digital ; $c 24 cm.
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.
520 $a 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.
650 0 $a Application software $x Development. $3 539563
650 0 $a Open source software. $3 581998
650 0 $a Big data. $3 2045508
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Big Data. $3 3134868
650 2 4 $a Computing Methodologies. $3 830243
650 2 4 $a Open Source. $3 2210577
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-1-4842-3012-1
950 $a Professional and Applied Computing (Springer-12059)