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Running TensorFlow 2 on Apple M1/M2 Macs
Jan 14, 2023 • 3 minutes


I ran into issues when getting started with Tensorflow 2.0 on macOS M1, this post may help others who are trying to get started with TensorFlow 2.0 on Apple M1 Macs. This post is a work in progress and will be updated as I learn more.

macOS M1 machine come with GPU framework, Metal supports new M1 CPUs with Graphics capabilities as well. Over the last 2 years, Apple has been working towards making M1 platform more accessible for Machine Learning.

Also Running machine learning frameworks on laptops with smaller loads has several benefits. One of the main benefits is the ability to work on projects on-the-go, without being tied to a dedicated workstation or server. Additionally, on laptops smaller loads typically have less power consumption.

Apple’s M1 arm based chips, which is used in the latest MacBooks, is designed to be a powerful and efficient processor for running machine learning workloads. The macos-metal and macos-tensorflow frameworks enable running TensorFlow 2 on M1 machines, which provides even better performance than running it on traditional x86 processors because it’s SoC rather than having different components plugging in together - read more here

Anyway, getting it right is little bit tricky, but here are simple things you can do get it write.

NOTE: Please note that Code snippets wrap in small screen, and also take care that code snippets are for reference only, and may not work as is in the future.