Book Image

Hands-On Deep Learning with Apache Spark

By : Guglielmo Iozzia
Book Image

Hands-On Deep Learning with Apache Spark

By: Guglielmo Iozzia

Overview of this book

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.
Table of Contents (19 chapters)
Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

Setup of a distributed environment with DeepLearning4j

This section explains some tricks to do when setting up a production environment for DL4J neural network model training and execution.

Memory management

In Chapter 7, Training Neural Networks with Spark, in the Performance considerations section, we learned how DL4J handles memory when training or running a model. Because it relies on ND4J, it also utilizes off-heap memory and not only heap memory. Being off-heap, it means that it is outside the scope managed by the JVM's Garbage Collection (GC) mechanism (the memory is allocated outside the JVM). At the JVM level, there are only pointers to off-heap memory locations; they can be passed to the C++ code via the Java...