Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Hands-On GPU Computing with Python
  • Table Of Contents Toc
Hands-On GPU Computing with Python

Hands-On GPU Computing with Python

By : Avimanyu Bandyopadhyay
2 (1)
close
close
Hands-On GPU Computing with Python

Hands-On GPU Computing with Python

2 (1)
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)
close
close
Lock 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

Containerization on GPU-Enabled Platforms

In this new chapter, we will continue our exploration with GPUs while specifically focusing on user accessibility. You will learn about different environments to choose from when setting up a GPU-based programmable platform. These environments will be compared and discussed to help you decide on the most suitable one pertaining to usability and different situations or conditions. Following this, system-wide and virtual environments will be explained. Their advantages and disadvantages will also be explored.

Virtualenv, which is similar to Conda, will be discussed as an example of a closed environment separate from the base system. We will also look at a scenario where both system-wide and Virtualenv packages can co-exist and work together when accessed from a virtual environment.

Exploring further, containers such as Docker and Kubernetes...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Hands-On GPU Computing with Python
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon