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Hands-On GPU Computing with Python

You're reading from   Hands-On GPU Computing with Python Explore the capabilities of GPUs for solving high performance computational problems

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Length 452 pages
Edition 1st Edition
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Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Author Profile Icon Avimanyu Bandyopadhyay
Avimanyu Bandyopadhyay
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
2. Introducing GPU Computing FREE CHAPTER 3. Designing a GPU Computing Strategy 4. Setting Up a GPU Computing Platform with NVIDIA and AMD 5. Section 2: Hands-On Development with GPU Programming
6. Fundamentals of GPU Programming 7. Setting Up Your Environment for GPU Programming 8. Working with CUDA and PyCUDA 9. Working with ROCm and PyOpenCL 10. Working with Anaconda, CuPy, and Numba for GPUs 11. Section 3: Containerization and Machine Learning with GPU-Powered Python
12. Containerization on GPU-Enabled Platforms 13. Accelerated Machine Learning on GPUs 14. GPU Acceleration for Scientific Applications Using DeepChem 15. Other Books You May Enjoy Appendix A

Installing PyCUDA for Python within an existing CUDA environment

Since we have already learned about CUDA's installation and implementation, it will now be easier for us to get started with our PyCUDA installation procedure for Python. You also do not need to install Python as it is already available (both 2.x and 3.x) with a freshly installed version of the Ubuntu 18.04 Linux operating system.

As we have also learned about Anaconda and its setup, we can also make use of Python 2.x or 3.x, which is readily available with an existing Anaconda configuration. Setting up PyCUDA will enable implementing CUDA kernels within your existing Python setup of choice and then computing with it on your NVIDIA GPU.

There are primarily two methods of installation.

Anaconda-based installation...

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