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

Revisiting our computational exercises with a machine learning approach

In this section, let's apply all the knowledge we've acquired so far. Try using a real-world dataset, as discussed in Chapter 6, Working with CUDA and PyCUDA, and use the Solution Assistance section to get started with the following exercises to step up your machine learning game:

  1. Use TensorFlow or PyTorch to implement Karl Pearson's correlation coefficient. Based on the computed coefficient, use machine learning to predict the probability of a certain population in a region to be affected with a correlated disease. You can also use image datasets of tobacco and its linked diseases to widen the scope of the study.
  2. Create a machine learning model with TensorFlow or PyTorch for the prediction of diabetes. Use real-world data after testing your model.
  3. Create a machine learning model with TensorFlow...