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Hands-On GPU Computing with Python

You're reading from  Hands-On GPU Computing with Python

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Profile icon Avimanyu Bandyopadhyay

Table of Contents (17) Chapters

Preface Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
Introducing GPU Computing Designing a GPU Computing Strategy Setting Up a GPU Computing Platform with NVIDIA and AMD Section 2: Hands-On Development with GPU Programming
Fundamentals of GPU Programming Setting Up Your Environment for GPU Programming Working with CUDA and PyCUDA Working with ROCm and PyOpenCL Working with Anaconda, CuPy, and Numba for GPUs Section 3: Containerization and Machine Learning with GPU-Powered Python
Containerization on GPU-Enabled Platforms Accelerated Machine Learning on GPUs GPU Acceleration for Scientific Applications Using DeepChem Other Books You May Enjoy Appendix A

Summary

In this chapter, programmable environments were discussed with a perspective on system-wide and virtual environments. Scenarios where a particular option is preferred were discussed. The directory structures of both system-wide and virtual environments were explored, in addition to their advantages and disadvantages. The containerization concept was introduced as an evolution from virtualization. Finally, local and cloud containers were explored in detail with a hands-on approach.

You are now familiar with the different environments to choose from in order to set up and use a development platform with GPUs. With Virtualenv and VirtualBox, you can now set up your own isolated development environments. The benefits of using Virtualenv will help prepare you to customize future preferences when setting up a closed programmable environment. Depending upon your usability requirements...

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