How to Install TensorFlow on M1 macOS Monterey

TensorFlow is an open-source machine learning library originally developed by the Machine Intelligence team at Google Brain for building and training deep neural networks. I chose TensorFlow because it is robust, efficient, and integrates natively with Python. Back in 2022, when I first wrote this guide, installing TensorFlow on Apple Silicon (M1) required Apple’s arm64-native packages — tensorflow-macos and tensorflow-metal — because the generic pip install tensorflow build was not yet optimised for the M1 architecture. That has since changed: from TensorFlow 2.13 onwards, pip install tensorflow ships native arm64 support directly, so you only need the separate tensorflow-macos package if you are pinned to TensorFlow 2.12 or earlier — you still need tensorflow-metal either way for GPU acceleration. See Apple’s tensorflow-metal guide for the current install steps.

This guide installs Xcode, Homebrew, Miniforge (Conda), TensorFlow, and Jupyter on M1 macOS Monterey, then benchmarks CPU versus GPU training on the MNIST dataset.

Installing Xcode Command Line Tools on M1

I had a new computer, so I started by downloading and installing Xcode from the App Store, which provides the compilers and command line tools required to build native arm64 packages.

Installing Homebrew on Apple Silicon

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Installing Miniforge and Conda for arm64

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Installing tensorflow-macos and tensorflow-metal in Conda

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Testing TensorFlow GPU vs CPU on the MNIST Dataset

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Common TensorFlow M1 Installation Errors and Fixes

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Conclusion: TensorFlow on M1 with GPU Acceleration

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