Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
About Packt


Careful considerations need to be made while implementing multiprocessing reduction operators in Python, especially if the program utilizes task queues and result queues to facilitate communication across the consumer processes.

The operations of various real-world problems resemble reduction operators, and the use of concurrency and parallelism for these problems could greatly improve efficiency and thus productivity of the programs processing them. It is therefore important to be able to identify these problems, and relate back to the concept of reduction operators to implement their solutions.

In the next chapter, we will be discussing a specific real-world application for multiprocessing programs in Python: image processing. We will be going over the basic ideas behind image processing and how concurrency—specifically multiprocessing—could be applied to image-processing applications.