Create distributed applications with clever design patterns to solve complex problems
Key Features:
- Set up and run distributed algorithms on a cluster using Dask and PySpark
- Master skills to accurately implement concurrency in your code
- Gain practical experience of Python design patterns with real-world examples
Book Description:
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
What You Will Learn:
- Use NumPy and pandas to import and manipulate datasets
- Achieve native performance with Cython and Numba
- Write asynchronous code using asyncio and RxPy
- Design highly scalable programs with application scaffolding
- Explore abstract methods to maintain data consistency
- Clone objects using the prototype pattern
- Use the adapter pattern to make incompatible interfaces compatible
- Employ the strategy pattern to dynamically choose an algorithm
Who this book is for:
This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Dr. Gabriele Lanaro is passionate about good software and is the author of the chemlab and chemview open source packages. His interests span machine learning, numerical computing visualization, and web technologies. In 2013, he authored the first edition of the book High Performance Python Programming. He has been conducting research to study the formation and growth of crystals using medium and large-scale computer simulations. In 2017, he obtained his PhD in theoretical chemistry.
Quan Nguyen, the author of the first edition of this book, is a Python programmer with a strong passion for machine learning. He holds a dual degree in mathematics and computer science, with a minor in philosophy, earned from DePauw University. Quan is deeply involved in the Python community and has authored multiple Python books, contributing to the Python Software Foundation and regularly sharing insights on DataScience portal. He is currently pursuing a Ph.D. in computer science at Washington University in St. Louis.
Sakis Kasampalis is a software architect living in the Netherlands. He is not dogmatic about particular programming languages and tools; his principle is that the right tool should be used for the right job. One of his favorite tools is Python because he finds it very productive. Sakis was also the technical reviewer of Mastering Object-oriented Python and Learning Python Design Patterns, published by Packt Publishing.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Paperback. Condizione: Good. With this Learning Path, you will gain complete knowledge to solve problems by building high performing applications loaded with asynchronous, multithreaded code and proven design patterns. Codice articolo CIN1838551212G
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Paperback. Condizione: New. Create distributed applications with clever design patterns to solve complex problemsKey FeaturesSet up and run distributed algorithms on a cluster using Dask and PySparkMaster skills to accurately implement concurrency in your codeGain practical experience of Python design patterns with real-world examplesBook DescriptionThis 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 LanaroMastering Concurrency in Python by Quan NguyenMastering Python Design Patterns by Sakis KasampalisWhat you will learnUse NumPy and pandas to import and manipulate datasetsAchieve native performance with Cython and NumbaWrite asynchronous code using asyncio and RxPyDesign highly scalable programs with application scaffoldingExplore abstract methods to maintain data consistencyClone objects using the prototype patternUse the adapter pattern to make incompatible interfaces compatibleEmploy the strategy pattern to dynamically choose an algorithmWho this book is forThis Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path. Codice articolo LU-9781838551216
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