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 Jupyter Notebook and Jupyter Lab

The installation of Jupyter Notebook and Jupyter Lab is very simple, since we have already learned how to install Anaconda.

For a system-wide installation for both of them on Anaconda, open a Terminal and run the following command with conda:

$ conda install jupyter jupyterlab

For a separate installation, you can first create a virtual environment with conda and then use the preceding command. Here, we use jupyterworld as the name of the virtual environment. Enter y to proceed:

$ conda create --name jupyterworld
---
proceed ([y]/n)? y
$ conda activate jupyterworld
(jupyterworld)$ conda install jupyter jupyterlab

To run Jupyter Notebook, use the following command:

jupyter notebook

To run Jupyter Lab, use the following command:

jupyter lab

Your default web browser will launch it as soon as you enter the command.

Here is how the web-based IDE...