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Guide to Install Tensorflow and PyTorch for Apple Silicon

Most ML Frameworks rely on NVIDIA's CUDA to tap into the parallel processing capabilities offered by NVIDIA GPUs.

In contrast, Apple has a different GPU architecture for its M1 and M2 CPUs. While built upon traditional GPU principles, this architecture is finely tuned to meet Apple's specific requirements.

To enable GPU support for TensorFlow and PyTorch on MacOS, the following prerequisites must be met:

  • Mac Computers with Apple Silicon
  • macOS 12.0 or later
  • Python 3.8
  • Xcode CLI tools (install them using xcode-select — install)

Also, make sure you have pip (or install pip): 
python3 -m pip install --upgrade pip

Install Tensorflow 

SYSTEM_VERSION_COMPAT=0 python3 -m pip install tensorflow-macos
SYSTEM_VERSION_COMPAT=0 python3 -m pip install tensorflow-metal
SYSTEM_VERSION_COMPAT=0 python3 -m pip install tensorflow_datasets

You can check that TensorFlow is now using GPU:

import tensorflow as tf
import tensorflow_datasets as tfds
print("TensorFlow version:", tf.__version__)
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

tf.config.list_physical_devices('GPU')

Check out mps-benchmark.ipynb for the LeNet-5 training code to verify it is using GPU.

Install PyTorch 

curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
sh Miniconda3-latest-MacOSX-x86_64.sh

and then you can simply install PyTorch as

SYSTEM_VERSION_COMPAT=0 python3 -m pip install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu

Again, check out mps-benchmark.ipynb for the LeNet-5 training code to verify it is using GPU.