Book Image

Python High Performance, Second Edition - Second Edition

By : Dr. Gabriele Lanaro
Book Image

Python High Performance, Second Edition - Second Edition

By: Dr. Gabriele Lanaro

Overview of this book

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn 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. By the end of the book, readers will have learned to achieve performance and scale from their Python applications.
Table of Contents (10 chapters)

The asyncio framework

By now, you should have a solid foundation of how concurrency works, and how to use callbacks and futures. We can now move on and learn how to use the asyncio package present in the standard library since version 3.4. We will also explore the brand new async/await syntax to deal with asynchronous programming in a very natural way.

As a first example, we will see how to retrieve and execute a simple callback using asyncio. The asyncio loop can be retrieved by calling the asyncio.get_event_loop() function. We can schedule a callback for execution using loop.call_later that takes a delay in seconds and a callback. We can also use the loop.stop method to halt the loop and exit the program. To start processing the scheduled call, it is necessary to start the loop, which can be done using loop.run_forever. The following example demonstrates the usage of these basic methods by scheduling a callback...