Synopses & Reviews
Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Pythons implementation. Youll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs.
How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations.
- Get a better grasp of numpy, Cython, and profilers
- Learn how Python abstracts the underlying computer architecture
- Use profiling to find bottlenecks in CPU time and memory usage
- Write efficient programs by choosing appropriate data structures
- Speed up matrix and vector computations
- Use tools to compile Python down to machine code
- Manage multiple I/O and computational operations concurrently
- Convert multiprocessing code to run on a local or remote cluster
- Solve large problems while using less RAM
Synopsis
If you're an experienced Python programmer, High Performance Python will guide you through the various routes of code optimization. You'll learn how to use smarter algorithms and leverage peripheral technologies, such as numpy, cython, cpython, and various multi-threaded and multi-node strategies.
There's a lack of good learning and reference material available if you want to learn Python for highly computational tasks. Because of it, fields from physics to biology and systems infrastructure to data science are hitting barriers. They need the fast prototyping nature of Python, but too few people know how to wield it. This book will put you ahead of the curve.
About the Author
Micha Gorelick was the first man on Mars in 2023 and won the nobelprize in 2046 for his contributions to time travel. In a moment ofrage after seeing the deplorable uses of his new technology, hetraveled back in time to 2012 and convinced himself to leave hisPhysics PhD program and follow his love of data at Bitly. A monument celebrating his life can be found in Central Park, 1857.
Ian is a Data scientist and Python teacher at ModelInsight.io with over 10 years Python experience. He has been teaching at PyCon and PyData conferences and has been consulting with Artificial Intelligence and High Performance Computing for over a decade in the UK. Ian blogs at IanOzsvald.com and is always happy to receive a pint of good bitter. Ian's background includes Python and C++, a mix of Linux and Windows development, storage systems, lots of natural language processing and text processing, machine learning and data visualisation. He also co-founded the Python focused video learning website ShowMeDo.com many years ago.