Название: High Performance Python: Practical Performant Programming for Humans, 3rd Edition (Early Release)
Автор: Micha Gorelick, Ian Oszvald
Издательство: O’Reilly Media, Inc.
Год: 2024-07-26
Страниц: 226
Язык: английский
Формат: pdf, 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 clusters? Or build a system that scales up and down without losing reliability? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for social media analytics, productionized Machine Learning, and more.

Programming computers can be thought of as moving bits of data and transforming them in special ways to achieve a particular result. However, these actions have a time cost. Consequently, high performance programming can be thought of as the act of minimizing these operations either by reducing the overhead (i.e., writing more efficient code) or by changing the way that we do these operations to make each one more meaningful (i.e., finding a more suitable algorithm).

Let’s focus on reducing the overhead in code in order to gain more insight into the actual hardware on which we are moving these bits. This may seem like a futile exercise, since Python works quite hard to abstract away direct interactions with the hardware. However, by understanding both the best way that bits can be moved in the real hardware and the ways that Python’s abstractions force your bits to move, you can make progress toward writing high performance programs in Python.

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
Deploy code faster using tools like Docker