All the examples have been implemented using Python version 2.7 and 3.5 on an Ubuntu Linux 64 bit, including the TensorFlow library version 1.0.1. However, in the book, we showed the source code with only Python 2.7 compatible. Source codes that are Python 3.5+ compatible can be downloaded from the Packt repository. You will also need the following Python modules (preferably the latest versions):
- Spark 2.0.0 (or higher)
- Hadoop 2.7 (or higher)
- Java (JDK and JRE) 1.7+/1.8+
- Scala 2.11.x (or higher)
- Python 2.7+/3.4+
- R 3.1+ and RStudio 1.0.143 (or higher)
- Eclipse Mars, Oxygen, or Luna (latest)
- Maven Eclipse plugin (2.9 or higher)
- Maven compiler plugin for Eclipse (2.3.2 or higher)
- Maven assembly plugin for Eclipse (2.4.1 or higher)
Operating system: Linux distributions are preferable (including Debian, Ubuntu, Fedora, RHEL, and CentOS) and to be more specific, for Ubuntu it is recommended to have a complete 14.04 (LTS) 64-bit (or later) installation, VMWare player 12, or Virtual box. You can run Spark jobs on Windows (XP/7/8/10) or Mac OS X (10.4.7+).
Hardware configuration: Processor Core i3, Core i5 (recommended), or Core i7 (to get the best results). However, multicore processing will provide faster data processing and scalability. You will need least 8-16 GB RAM (recommended) for a standalone mode and at least 32 GB RAM for a single VM--and higher for cluster. You will also need enough storage for running heavy jobs (depending on the dataset size you will be handling), and preferably at least 50 GB of free disk storage (for standalone word missing and for an SQL warehouse).
