High-Performance Computing with Python 3.x: Get to grips with various techniques and tools to provide High-Performance Computing in Python 3.x

Mohammed Kashif

$137.99

Video Feb 2019 4hrs 12mins 1st Edition

Mohammed Kashif

$137.99

Video Feb 2019 4hrs 12mins 1st Edition

What do you get with a video?

Download this video in MP4 format

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

View table of contents

Download Code

Key benefits

Master using NumPy, SciPy, and Cython to speed up your numerical computations.
Leverage the power of multiprocessing and multithreading in Python for parallelism.
Master using Dask to handle large data in a distributed setting and reactive applications in Python.

Description

Python is a versatile programming language. Many industries are now using Python for high-performance computing projects. This course will teach you how to use Python on parallel architectures. You'll learn to use the power of NumPy, SciPy, and Cython to speed up computation. Then you will get to grips with optimizing critical parts of the kernel using various tools. You will also learn how to optimize your programmer using Numba. You'll learn how to perform large-scale computations using Dask and implement distributed applications in Python; finally, you'll construct robust and responsive apps using Reactive programming. By the end, you will have gained a solid knowledge of the most common tools to get you started on HPC with Python. All code files are located on GitHub at this link https://github.com/PacktPublishing/High-Performance-Computing-with-Python-3.x

Who is this book for?

This course will help Python Programmers, Data Analysts and aspiring Data Science professionals familiar with basic Python programming to extend their skillset so as to scale their code and improve their code performance.

What you will learn

Use lambda expressions, generators, and iterators to speed up your code.
A solid understanding of multiprocessing and multithreading in Python.
Optimize performance and efficiency by leveraging NumPy, SciPy, and Cython for numerical computations.
Load large data using Dask in a distributed setting.
Leverage the power of Numba to make your Python programs run faster.
Build reactive applications using Python.

What do you get with a video?