Book Image

Hands-On GPU Computing with Python

By : Avimanyu Bandyopadhyay
Book Image

Hands-On GPU Computing with Python

By: Avimanyu Bandyopadhyay

Overview of this book

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 begins by introducing GPU computing and explaining the GPU architecture and programming models. You will learn, by example, how to perform GPU programming with Python, and look at using integrations such as PyCUDA, PyOpenCL, CuPy, and Numba with Anaconda for various tasks such as machine learning and data mining. In addition to this, you will get to grips with GPU workflows, 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.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
5
Section 2: Hands-On Development with GPU Programming
11
Section 3: Containerization and Machine Learning with GPU-Powered Python

Installing TensorFlow and PyTorch for GPUs

Since we have already learned about CUDA's installation and implementation, it will now be easier for us to get started on our TensorFlow and PyTorch installation procedure. Additionally, we also need cuDNN to be installed, which is the predefined deep neural network library for CUDA. To be able to download the library, you have to fill in a free registration at https://developer.nvidia.com/, which is the official web portal for the NVIDIA Developer program.

Installing cuDNN

In this section, we are going to install cuDNN 7.4.2 for both TensorFlow and PyTorch. Follow these steps to get started:

  1. Download the corresponding archive from the cuDNN repository:
  1. Use a Terminal and...