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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 FREE CHAPTER
2. Introducing GPU Computing 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

Configuring PyOpenCL on your Python IDE

To configure PyOpenCL with PyCharm, we again have to follow up the two ways of installation we discussed in the previous section. The following steps are independent of the previous chapter and can be used as a standalone reference guide for getting started with PyOpenCL directly.

Conda-based virtual environment

Now, let's create a virtual environment with Conda as a new PyCharm pure Python project:

  1. Launch PyCharm Professional Edition:
  1. Choose New Project from the PyCharm main menu. Skip this step if you've already created a project:
  1. Create a new virtual environment with Conda if not already present:
  1. Wait while the Conda environment gets created:

After creating the...

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