Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Setting up the VM for this chapter


As machine learning needs a lot of computational power, in order to save some resources (especially memory) we will use the Spark environment not backed by YARN in this chapter. This mode of operation is named standalone and creates a Spark node without cluster functionalities; all the processing will be on the driver machine and won't be shared. Don't worry; the code that we will see in this chapter will work in a cluster environment as well.

In order to operate this way, perform the following steps:

  1. Turn on the virtual machine using the vagrant up command.

  2. Access the virtual machine when it's ready, with vagrant ssh.

  3. Launch Spark standalone mode with the IPython Notebook from inside the virtual machine with ./start_jupyter.sh.

  4. Open a browser pointing to http://localhost:8888.

To turn it off, use the Ctrl + C keys to exit the IPython Notebook and vagrant halt to turn off the virtual machine.

Note

Note that, even in this configuration, you can access the Spark...