CUDA Programming in Python

Do you want faster Python code? In this tutorial we demonstrate how to use a Just In Time (JIT) Compiler for Python. You can turn regular Python into fast machine code, and write CUDA code for the GPU in Python too! JetsonHacks article: https://wp.me/p7ZgI9-3OB Here we give a high level introduction to using Numba, a JIT compiler for Python. Numba is a cross platform solution. Numba can generate machine code for those crucial time constrained functions. In addition, you can use Numba to generate CUDA code directly from Python code! We're running this demonstration on the NVIDIA AGX Orin Developer Kit, the premier computer for edge computing. NVIDIA Jetson AGX Orin Developer Kit: https://amzn.to/3U06UIn NVIDIA Jetson Orin Nano Developer Kit: https://amzn.to/3vORvAi Code demonstrated: https://github.com/jetsonhacks/cuda-using-numba.git 00:00 Introduction 00:11 Timing a Sobel Filter 04:06 Speed Comparison CPU vs GPU 04:22 Writing CUDA in Python 07:19 Overview of how JIT works Join this channel to get access to perks: https://www.youtube.com/channel/UCQs0lwV6E4p7LQaGJ6fgy5Q/join As an Amazon Associate I earn from qualifying purchases. Visit the JetsonHacks storefront on Amazon: https://www.amazon.com/shop/jetsonhacks Visit the website at https://jetsonhacks.com Sign up for the newsletter! https://newsletter.jetsonhacks.com Github accounts: https://github.com/jetsonhacks https://github.com/jetsonhacksnano Twitter: http://twitter.com/jetsonhacks Some of these links here are affiliate links. As an Amazon Associate I earn from qualifying purchases at no extra cost to you.