Natural Language Processing in Action

13 Scaling up (optimization, parallelization, and batch processing)

This chapter covers

• Scaling up an NLP pipeline
• Speeding up search with indexing
• Batch processing to reduce your memory footprint
• Parallelization to speed up NLP
• Running NLP model training on a GPU

In chapter 12, you learned how to use all the tools in your NLP toolbox to build an NLP pipeline capable of carrying on a conversation. We demonstrated crude examples of this chatbot dialog capability on small datasets. The humanness, or IQ, of your dialog system seems to be limited by the data you train it with. Most of the NLP approaches you’ve learned give better and better results, if you can scale them up to handle larger datasets.

You may have noticed that your computer bogs ...