Introduction

NLTK is great for in-memory, single-processor natural language processing. However, there are times when you have a lot of data to process and want to take advantage of multiple CPUs, multicore CPUs, and even multiple computers. Or, you might want to store frequencies and probabilities in a persistent, shared database so multiple processes can access it simultaneously. For the first case, we'll be using execnet to do parallel and distributed processing with NLTK. For the second case, you'll learn how to use the Redis data structure server/database to store frequency distributions and more.