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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
Concepts
Author (1):
Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Profile icon Avimanyu Bandyopadhyay
LinkedIn
Avimanyu Bandyopadhyay is currently pursuing a PhD degree in Bioinformatics based on applied GPU computing in Computational Biology at Heritage Institute of Technology, Kolkata, India. Since 2014, he developed a keen interest in GPU computing, and used CUDA for his master's thesis. He has experience as a systems administrator as well, particularly on the Linux platform. Avimanyu is also a scientific writer, technology communicator, and a passionate gamer. He has published technical writing on open source computing and has actively participated in NVIDIA's GPU computing conferences since 2016. A big-time Linux fan, he strongly believes in the significance of Linux and an open source approach in scientific research. Deep learning with GPUs is his new passion!
See other products by Avimanyu Bandyopadhyay

Table of Contents (17) Chapters

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
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

Installing TensorFlow and PyTorch for GPUs

Since we have already learned about CUDA's installation and implementation, it will now be easier for us to get started on our TensorFlow and PyTorch installation procedure. Additionally, we also need cuDNN to be installed, which is the predefined deep neural network library for CUDA. To be able to download the library, you have to fill in a free registration at https://developer.nvidia.com/, which is the official web portal for the NVIDIA Developer program.

Installing cuDNN

In this section, we are going to install cuDNN 7.4.2 for both TensorFlow and PyTorch. Follow these steps to get started:

  1. Download the corresponding archive from the cuDNN repository:
  1. Use a Terminal and...
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Authors (1)

Profile icon Avimanyu Bandyopadhyay
LinkedIn
Avimanyu Bandyopadhyay is currently pursuing a PhD degree in Bioinformatics based on applied GPU computing in Computational Biology at Heritage Institute of Technology, Kolkata, India. Since 2014, he developed a keen interest in GPU computing, and used CUDA for his master's thesis. He has experience as a systems administrator as well, particularly on the Linux platform. Avimanyu is also a scientific writer, technology communicator, and a passionate gamer. He has published technical writing on open source computing and has actively participated in NVIDIA's GPU computing conferences since 2016. A big-time Linux fan, he strongly believes in the significance of Linux and an open source approach in scientific research. Deep learning with GPUs is his new passion!
Read more
See other products by Avimanyu Bandyopadhyay

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