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-u...

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:
   / @jetsonhacks  

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:   / jetsonhacks  

Some of these links here are affiliate links. As an Amazon Associate I earn from qualifying purchases at no extra cost to you.