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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 Python’s implementation. You’ll 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
- Sales Rank: #61915 in Books
- Published on: 2014-09-20
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 7.00" w x .75" l, .0 pounds
- Binding: Paperback
- 370 pages
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. He then went backto the 2000s to study Astronomy, teach scientific computing and workon data at bitly. Then he helped start Fast Forward Labs as a residentmad scientist. There he worked on many issues from machine learning toperformant stream algorithms. A monument celebrating his life can befound in Central Park, 1857.
Ian Ozsvald is a Data scientist and teacher at ModelInsight.io withover ten years of Python experience. He’s taught high performancePython at the PyCon and PyData conferences and has been consulting ondata science and high performance computing for years in the UK.
Most helpful customer reviews
21 of 22 people found the following review helpful.
Good survey of tools and methods to speed up your Python programs
By Zach Jones
One of the big draws of the Python programming language is that it is very easy to develop something relatively complex quite rapidly. However, Python is much more than a prototyping language, and this book is a great resource to help you think about how you approach problems in Python, as well as tracking down and improving bottlenecks in your code.
This book is definitely not going to teach you Python. There are many other tutorials and references out there to learn about the language, and this book assumes you are already a proficient Python programmer and will be able to read and understand the code examples they provide. This book is about tuning your Python code to run faster.
The progression of the chapters is very logical, and some of the same toy problems re-appear throughout the book as additional optimizations provide even greater efficiency improvements. The book introduces a large number of tools, and it mostly gives you an idea of what the tool is and why you might consider it. To really use any of the tools in practice, you'll want to reference online documentation, but this book gives you a good idea of where to start looking.
I was particularly interested in reading the "Clusters and Job Queues" chapter before I got the book, and it helped guide me to an IPython.parallel solution that fits my current problem quite nicely, as well as provide some other tools I may investigate in the future.
The authors recommend the Anaconda Python distribution by Continuum Analytics on several occasions, and I definitely agree. Some of the tools and techniques in the book use only the Standard Library, but most of the more advanced topics require external modules. Many of the modules referenced (numpy, Cython, Tornado, & IPython to name just a few) are included in the Anaconda distribution as one simple download.
This book's use is twofold. First, it is worth a full read-through for the discussion of the various things that tend to slow down Python code (or code in general) and what kinds of approaches you should be aware of. Second, it provides good, brief examples of many different tools in practice, as well as listing other recommended resources at the end of each chapter, allowing it to serve as a good reference text.
One point the authors make repeatedly is that you must consider the trade between code execution time and development velocity. Many of the things you can do to speed up your code with make it considerably harder to understand and work with in the future. It's important to always have proof that you are optimizing the right portions of code and that the benefits are worth it. They help you to look for the "big wins" where you can get drastic speed improvements with minimal effort and complexity.
Disclaimer: I received a free Ebook copy of this work under the O'Reilly Blogger Review Program. I also happened to like it so much that I bought a hard copy as well so I can have it on my reference shelf at work.
11 of 12 people found the following review helpful.
Covers High Performance computing, concurency issues and server applications
By Jerome Kieffer
Great book, the content is useful everywhere for anybody writing Python code while most demo are "linux-centric".
It exposes a very good method, based on actual profiling. CPU and memory profiling is very well explained with actual recipes.
I liked the introduction of all Python container with their strengths and weaknesses, as well as the difference to C++.
The chapter on C-interface to get ultimate CPU-speed is very long and complete, it would be useful to cut it in two.
I liked also the chapter introducing the recent AsyncIO and the comparison to tornado and gevent.
Finally, the last chapter gives some useful examples on how Python is used in industry to solve "real world problems"
Owning a "preview" version, there are mistakes and misspelling still present, but they should be corrected in the final version.
9 of 10 people found the following review helpful.
A superb reference for any proficient in Python
By Keho
Many useful code examples to experiment and learn from are unfortunately tailored to run only on linux platforms, even though Python and related tools are ubiquitously supported in Windows and MAC. Otherwise, the book is a superb addition in one's data-science library, especially for those already proficient in Python.
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