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[center]Hands-On GPU Computing with Python | Avimanyu Bandyopadhyay | Packt Publishing | [/align]

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 GPUsBook 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...

Explore the capabilities of GPUs for solving high performance computational problems

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.

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Hands-On GPU Computing with Python.epub (22.64 MB)


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