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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 PyTorch on PyCharm and Google Colab

In this section, we will learn how to configure PyTorch on PyCharm and Google Colab. We will look at all the steps and commands involved in a sequential manner. Let's read on.

Using PyTorch on PyCharm

The next 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 PyTorch because the procedure is very similar. To configure PyTorch with PyCharm, we again focus on our Conda-based installation:

  1. Create a Pure Python project within a new local Conda environment (skip this step if you've already done this):
  1. Wait for the environment to be created:
  1. After creating the Conda environment, you...
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