Название: High Performance Python: Practical Performant Programming for Humans, 3rd Edition (Final Release)
Автор: Micha Gorelick, Ian Oszvald
Издательство: O’Reilly Media, Inc.
Год: 2025
Страниц: 586
Язык: английский
Формат: epub
Размер: 10.1 MB

Your Python code may run correctly, but what if you need it to run faster? This practical book shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By explaining the fundamental theory behind design choices, this expanded edition of High Performance Python helps experienced Python programmers gain a deeper understanding of Python's implementation.

How do you take advantage of multicore architectures or compilation? Or build a system that scales up beyond RAM limits or with a GPU? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for GenAI data extraction, productionized Machine Learning, and more.

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
Process DataFrames quickly with Pandas, Dask, and Polars
Speed up your neural networks and GPU computations
Use tools to compile Python down to machine code
Manage multiple I/O and computational operations concurrently
Convert multiprocessing code to run on local or remote clusters

Python is easy to learn. You’re probably here because now that your code runs correctly, you need it to run faster. You like the fact that your code is easy to modify and you can iterate with ideas quickly. The trade-off between easy to develop and runs as quickly as I need is a well-understood and often-bemoaned phenomenon. There are solutions.

Some people have serial processes that have to run faster. Others have problems that could take advantage of multicore architectures, clusters, or graphics processing units. Some need scalable systems that can process more or less as expediency and funds allow, without losing reliability. Others will realize that their coding techniques, often borrowed from other languages, perhaps aren’t as natural as examples they see from others.

In this book we will cover all of these topics, giving practical guidance for understanding bottlenecks and producing faster and more scalable solutions. We also include some war stories from those who went ahead of you, who took the knocks so you don’t have to.

Python is well suited for rapid development, production deployments, and scalable systems. The ecosystem is full of people who are working to make it scale on your behalf, leaving you more time to focus on the more challenging tasks around you.

Who This Book Is For:
You’ve used Python for long enough to have an idea about why certain things are slow and to have seen technologies like Cython, numpy, and PyPy being discussed as possible solutions. You might also have programmed with other languages and so know that there’s more than one way to solve a performance problem. While this book is primarily aimed at people with CPU-bound problems, we also look at data transfer and memory-bound solutions. Typically, these problems are faced by scientists, engineers, quants, and academics.

We also look at problems that a web developer might face, including the movement of data and the use of just-in-time (JIT) compilers like PyPy and asynchronous I/O for easy-win performance gains. It might help if you have a background in C (or C++, or maybe Java), but it isn’t a prerequisite. Python’s most common interpreter (CPython—the standard you normally get if you type Python at the command line) is written in C, and so the hooks and libraries all expose the gory inner C machinery. There are lots of other techniques that we cover that don’t assume any knowledge of C. You might also have a lower-level knowledge of the CPU, memory architecture, and data buses, but again, that’s not strictly necessary.

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