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

Virtualization

Now that we know about the basic differences between open and closed environments, along with their advantages and disadvantages, let's proceed further into the virtualization concept. This is essential before we move on to our primary discussion—containerization, which is the main theme of this chapter.

As you might be well aware now, virtualization is a way to run applications and operating systems in an isolated location, allocated on a physical hard disk and RAM. Physical hard disk space and RAM can be use to allocate resources and create multiple virtual environments. The physical space allocation is referred to as the host, whereas the virtual space allocations are referred to as guests.

In the earlier chapters, we discussed installation and configuration steps for different Python modules with Conda. All of those steps were in fact, ways to virtualize...