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Hands-On GPU Computing with Python - Explore the capabilities of GPUs for solving high performance computational problems - cover

Hands-On GPU Computing with Python - Explore the capabilities of GPUs for solving high performance computational problems

Avimanyu Bandyopadhyay

Publisher: Packt Publishing

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Summary

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate

 

Key Features



Understand effective synchronization strategies for faster processing using GPUs

Write parallel processing scripts with PyCuda and PyOpenCL

Learn to use the CUDA libraries like CuDNN  for deep learning on GPUs

 

Book Description


 
GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing.


 



 
This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you'll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance.


 



 
By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly.

 

What you will learn



Utilize Python libraries and frameworks for GPU acceleration

Set up a GPU-enabled programmable machine learning environment on your system with Anaconda

Deploy your machine learning system on cloud containers with illustrated examples

Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.

Perform data mining tasks with machine learning models on GPUs

Extend your knowledge of GPU computing in scientific applications

 

Who this book is for


 
Data Scientist, Machine Learning enthusiasts and professionals  who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.
Available since: 05/14/2019.
Print length: 452 pages.

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