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Advanced Python Programming

You're reading from   Advanced Python Programming Accelerate your Python programs using proven techniques and design patterns

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801814010
Length 606 pages
Edition 2nd Edition
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Author (1):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
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Table of Contents (32) Chapters Close

Preface 1. Section 1: Python-Native and Specialized Optimization
2. Chapter 1: Benchmarking and Profiling FREE CHAPTER 3. Chapter 2: Pure Python Optimizations 4. Chapter 3: Fast Array Operations with NumPy, Pandas, and Xarray 5. Chapter 4: C Performance with Cython 6. Chapter 5: Exploring Compilers 7. Chapter 6: Automatic Differentiation and Accelerated Linear Algebra for Machine Learning 8. Section 2: Concurrency and Parallelism
9. Chapter 7: Implementing Concurrency 10. Chapter 8: Parallel Processing 11. Chapter 9: Concurrent Web Requests 12. Chapter 10: Concurrent Image Processing 13. Chapter 11: Building Communication Channels with asyncio 14. Chapter 12: Deadlocks 15. Chapter 13: Starvation 16. Chapter 14: Race Conditions 17. Chapter 15: The Global Interpreter Lock 18. Section 3: Design Patterns in Python
19. Chapter 16: The Factory Pattern 20. Chapter 17: The Builder Pattern 21. Chapter 18: Other Creational Patterns 22. Chapter 19: The Adapter Pattern 23. Chapter 20: The Decorator Pattern 24. Chapter 21: The Bridge Pattern 25. Chapter 22: The Façade Pattern 26. Chapter 23: Other Structural Patterns 27. Chapter 24: The Chain of Responsibility Pattern 28. Chapter 25: The Command Pattern 29. Chapter 26: The Observer Pattern 30. Assessments 31. Other Books You May Enjoy

Automatic parallelism

Examples of packages that implement automatic parallelism are the (by now) familiar just-in-time (JIT) compilers numexpr and Numba. Other packages have been developed to automatically optimize and parallelize array and matrix-intensive expressions, which are crucial in specific numerical and machine learning (ML) applications.

Theano is a project that allows you to define a mathematical expression on arrays (more generally, tensors), and compile them to a fast language, such as C or C++. Many of the operations that Theano implements are parallelizable and can run on both the CPU and GPU.

TensorFlow is another library that, similar to Theano, is targeted toward array-intensive mathematical expressions but, rather than translating the expressions to specialized C code, executes the operations on an efficient C++ engine.

Both Theano and TensorFlow are ideal when the problem at hand can be expressed in a chain of matrix and element-wise operations (such as...

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