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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

Configuring CuPy on your Python IDE

The following steps are specific to the PyCharm IDE. But if you prefer a different IDE, you can still use these steps as a reference for setting up CuPy, because the procedure is very similar. To configure CuPy with PyCharm, we focus on our Conda-based installation:

  1. First, let's create a virtual environment with Conda as a new PyCharm pure Python project. Choose New Project... from the PyCharm main menu:
  1. Create a Pure Python project within a new local Conda environment, as shown in the following screenshot:
  1. Wait for the environment to be created, as shown:
  1. After creating the Conda environment, you will have a ready-to-use CuPy development environment, as shown in the following screenshot:

Now you can import cupy within your Python programs. As you can see, PyCharm Edu detects and recommends this as you begin to type import cupy...

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