Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Mastering Apache Spark 2.x
  • Table Of Contents Toc
Mastering Apache Spark 2.x

Mastering Apache Spark 2.x - Second Edition

By : Romeo Kienzler
4.5 (2)
close
close
Mastering Apache Spark 2.x

Mastering Apache Spark 2.x

4.5 (2)
By: Romeo Kienzler

Overview of this book

Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform. The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud. By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.
Table of Contents (15 chapters)
close
close
10
Deep Learning on Apache Spark with DeepLearning4j and H2O
chevron up

Deeplearning4j


Deeplearning4j is developed by a Silicon Valley startup called Skymind and can be found at www.deeplearning4j.org. It is open source and runs in many different environments, including Apache Spark.

The most important components of the framework are as follows:

  • Deeplearning4j runtime: This is the core module that allows you to define and execute all sorts of neural networks on top of Apache Spark, but it does not use Apache Spark directly; it uses a a tensor library similar to NumPy for Python.
  • ND4J/ND4S: This tensor library is really the heart of Deeplearning4j. It can also be used standalone and provides accelerated linear algebra on top of CPUs and GPUs. For porting code to a GPU, no code changes are required, since a JVM property configures the underlying execution engine, which can also be a CUDA backend for nVidia GPU cards.

In contrast to H2O, Deeplearning4j doesn't necessarily need any additional components to be installed, since it is a native Apache Spark application...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Mastering Apache Spark 2.x
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon