Code with me: Machine learning on a Macbook GPU (works for all M1, M2, M3) for a 10x speedup

Step aside, NVIDIA CUDA! Apple Macbooks now have powerful M1 M2 M3 chips that are great for machine learning. This is your complete guide on how to run Pytorch ML models on your Mac’s GPU, instead of the CPU or CUDA. A machine learning engineer walks you through the easy, simple code changes needed to tap into your GPU - with only 5 lines of code! As a result, you’ll see a 10-20x speedup when running or training your ML models 🚀 TIMESTAMPS 00:00 Hello nerd :3 01:24 Train Predict Checkpoint 06:07 Compiling Data types 09:27 On Device 11:24 Subscribe boo Connect with me ✨ www.instagram.com/@tam_trance (posting more content soon) www.tiktok.com/@thetechtrance    / @thetechtrance   #apple #machinelearning #gpu SOURCES U-Net Semantic Segmentation repo https://github.com/milesial/Pytorch-UNet U-Net Classifier-Free Diffusion Guidance paper https://arxiv.org/pdf/2207.12598 Apple product video materials by Apple Inc Background music Twilight Voyage by Ghostrifter http://bit.ly/ghostrifter-yt Creative Commons — Attribution-NoDerivs 3.0 Unported — CC BY-ND 3.0 Free Download: https://hypeddit.com/ahl2eq Disclaimer: This video contains clips from Apple Inc events, which are used and modified under fair use for educational/critical/commentary purposes. I do not claim ownership of the original materials used in this video. This content is not affiliated with, endorsed, sponsored, or specifically approved by Apple Inc and Apple Inc is not responsible for it. For more information about Apple’s original content, please visit their official website.