Book Image

Mastering Apache Spark 2.x - Second Edition

Book Image

Mastering Apache Spark 2.x - Second Edition

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 (21 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
10
Deep Learning on Apache Spark with DeepLearning4j and H2O

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...