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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

Developing your own deep learning framework like DeepChem – a brief outlook

Developing your own machine learning or deep learning framework requires perseverance, patience, and hard work. To get started with such an exciting new journey, you can start with a simple hands-on approach.

All developmental files for DeepChem installed with Conda are located at /home/user/miniconda3/envs/DeepChem/lib/python3.6/site-packages/deepchem:

Let's open and see the contents of __init__.py on PyCharm:

Open __init__.py:

So, here are the various modules of DeepChem:

As you can see here, this is only one file. Each of the directories that we see (the first screenshot in this section) contains code on every submodule. You can start checking all of these submodules (directories) and get started with the development of a DeepChem-based framework. A thorough study of each and every documented...

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