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

Using multiple processes

The standard multiprocessing module can be used to quickly parallelize simple tasks by spawning several processes, while avoiding the GIL problem. Its interface is easy to use and includes several utilities to handle task submission and synchronization.

The Process and Pool classes

You can create a process that runs independently by subclassing multiprocessing.Process. You can extend the __init__ method to initialize resources, and you can write the portion of the code that will be executed in a subprocess by implementing the method. In the following code, we define a Process class that will wait for one second and print its assigned id:

    import multiprocessing 
    import time 

    class Process(multiprocessing.Process): 
        def __init__(self, id): 
            super(Process, self).__init__() 
   = id 

        def run(self): 
            print("I'm the process with id: {}".format(

To spawn the...