This is the code repository for Hands-On GPU Computing with Python, published by Packt.
Explore the capabilities of GPUs for solving high performance computational problems
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 will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy, Numba, Tensorflow, Keras and PyTorch with Anaconda for various tasks such as machine learning, data mining and scientific computing. Going further, you will get to grips with GPU work flows, 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.
This book covers the following exciting features:
- Utilize Python libraries and frameworks for GPU acceleration
- Set up a GPU-enabled programmable machine learning environment on your system with Anaconda
- Deploy your machine learning system on cloud containers with illustrated examples
- Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.
- Perform data mining tasks with machine learning models on GPUs
- Extend your knowledge of GPU computing in scientific applications
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
// Run function on 500 Million elements on the CPU
begin = clock();
multiply(N, p, q);
end = clock();
cpu_time_used = ((double) (end - begin)) / CLOCKS_PER_SEC;
Following is what you need for this book: Data Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.
With the following software list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 2 - 11 | PyCharm Community Edition, PyCharm Educational Edition, PyCharm for Anaconda Community Edition, PyCharm Professional Edition, PyCharm for Anaconda Professional Edition, PyDev, Jupyter Notebook, Jupyter Lab, Eric, CUDA, ROCm, Anaconda, CuPy, Numba, Google Colaboratory, Tensorflow, PyTorch, DeepChem | Linux (preferably Ubuntu) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA [Packt] [Amazon]
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Learn OpenCV 4 By Building Projects - Second Edition [Packt] [Amazon]
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!
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