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

Chapter 13

What is a process? What are the core differences between a process and a thread?

A process is an instance of a specific computer program or software that is being executed by the operating system. A process contains both the program code and its current activities and interactions with other entities. More than one thread can be implemented within the same process to access and share memory or other resources, while different processes do not interact in this way.

What is multiprocessing? What are the core differences between multiprocessing and multithreading?

Multiprocessing refers to the execution of multiple concurrent processes from an operating system, in which each process is executed on a separate CPU, as opposed to a single process at any given time. Multithreading, on the other hand, is the execution of multiple threads, which can be within the same process.

What are the API options provided by the multiprocessing module?

The multiprocessing module provides APIs to the Process class, which contains the implementation of a process while offering methods to spawn and interact with processes using an API similar to the threading module. The module also provides the Pool class, which is mainly used to implement a pool of processes, each of which will carry out the tasks submitted.

What are the core differences between the Process class and the Pool class from the multiprocessing module?

The Pool class implements a pool of processes, each of which will carry out tasks submitted to a Pool object. Generally, the Pool class is more convenient than the Process class, especially if the results returned from your concurrent application should be ordered.

What are the options to determine the current process in a Python program?

The multiprocessing module provides the current_process() method, which will return the Process object that is currently running at any point of a program. Another way to keep track of running processes in your program is to look at the individual process IDs through the os module.

What are daemon processes? What are their purposes, in terms of waiting for processes in a multiprocessing program?

Daemon processes run in the background and do not block the main program from exiting. This specification is common when there is not an easy way for the main program to tell if it is appropriate to interrupt the process at any given time, or when exiting the main program without completing the worker does not affect the end result.

How can you terminate a process? Why is it sometimes acceptable to terminate processes?

The terminate() method from the multiprocessing.Process class offers a way to quickly terminate a process. If the processes in your program never interact with the shared resources, the terminate() method is considerably useful, especially if a process appears to be unresponsive or deadlocked.

What are the ways to facilitate interprocess communication in Python?

While locks are one of the most common synchronization primitives used for communication among threads, pipes and queues are the main way to communicate between different processes. Specifically, they provide message passing options to facilitate communication between processes: pipes for connections between two processes, and queues for multiple producers and consumers.