Setup Apple Silicon Mac for Machine Learning in 11 minutes (PyTorch edition)

Setup your Apple M1, M1 Pro, M1 Max or M1 Ultra Mac for data science and machine learning with PyTorch.

Get the code on GitHub – https://github.com/mrdbourke/pytorch-apple-silicon
PyTorch on Mac announcement blog post – https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Learn PyTorch – https://learnpytorch.io
Setup Apple M1 for TensorFlow – https://youtu.be/_1CaUOHhI6U

Other links:
Learn ML (beginner-friendly courses I teach) – https://www.mrdbourke.com/ml-courses/
ML courses/books I recommend – https://www.mrdbourke.com/ml-resources/
Read my novel Charlie Walks – https://www.charliewalks.com

Connect elsewhere:
Web – https://dbourke.link/web
Twitter – https://www.twitter.com/mrdbourke
Twitch – https://www.twitch.tv/mrdbourke
ArXiv channel (past streams) – https://dbourke.link/archive-channel
Get email updates on my work – https://dbourke.link/newsletter

Timestamps:
0:00 – Intro and what we’re covering
1:00 – Open Terminal
1:30 – Requirements
2:22 – 1. Download and install Homebrew
3:51 – 2. Download Miniforge3
4:35 – 3. Install Miniforge3 to get access to Conda
6:10 – 4. Restart Terminal
6:25 – 5. Create a directory to hold the environment/test PyTorch
6:40 – 6. Create a Conda environment and activate it
7:32 – 7. Install PyTorch for Mac
9:01 – 8. Install common data science packages (Jupyter, NumPy, pandas etc)
9:35 – 9. Start a Jupyter Notebook server and create a new notebook
10:00 – 10. Running import code and testing whether PyTorch has access to GPU
11:05 – 11. Seeing if PyTorch can send a tensor to the MPS device

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