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Hands-On GPU Computing with Python

Hands-On GPU Computing with Python

By : Avimanyu Bandyopadhyay
2 (1)
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Hands-On GPU Computing with Python

Hands-On GPU Computing with Python

2 (1)
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)
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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

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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