So go ahead and install them as follows: $ conda install -c conda-forge matplotlib -y $ conda install -c conda-forge scikit-learn -y $ conda install -c conda-forge opencv -y $ conda install -c conda-forge pandas -y Type the following command to install the tensorflow-metal package: $ python -m pip install tensorflow-metal Installing Other Essential Librariesīesides installing the TensorFlow libraries, you would also very likely need to install other packages like matplotlib, sklearn, OpenCV, Pandas, etc, for your machine learning and deep learning projects. Type the following command to install the TensorFlow for Mac OS: $ python -m pip install tensorflow-macos Install tensorflow-metal plugin Type the following command to install the dependencies needed by TensorFlow (the tensorflow-deps package): $ conda install -c apple tensorflow-deps Installing TensorFlow

If your machine has only one single environment, this step is optional. You can verify this by using this command: $ conda info -envs # conda environments: # base * /Users/weimenglee/miniforge3 Activating the EnvironmentĪctivate the base environment using the following command: $ source ~/miniforge3/bin/activate Once the installation is completed, a base Python environment is created for you. Once the Miniforge3-MacOSX-arm64.sh file is downloaded, open Terminal and type in the following commands: $ chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh $ sh ~/Downloads/Miniforge3-MacOSX-arm64.shįollow the installation instructions on-screen.

Instead of using Anaconda, you will use MiniForge, a lightweight Python interpreter with full access to the Conda ecosystem.Ĭonda is an open source package and environment management system that runs on Windows, Mac OS and Linux.įor Apple Silicon machines, you can directly download the MinForge installer from: I have listed the steps in the following sections. Installing TensorFlow on your Apple Silicon Mac is straight-forward. Steps to install TensorFlow on Apple Silicon Mac Note that without the tensorflow-metal package, your TensorFlow code would be still be able to run on your Apple Silicon Mac, just that TensorFlow won't be able to leverage the GPU of the M1 or M2 (it can only use the CPU). Essentially, this means your TensorFlow code is now able to take advantage of the powerful GPU available in the new Apple Silicon. The Metal framework gives your app direct access to a device’s graphics processing unit (GPU).

tensorflow-metal - a plugin to ensure that TensorFlow is able to run on Metal.

tensorflow-deps - the dependencies to run Tensorflow on arm64, e.g.

The tensorflow-macospackage relies on two other packages: You can learn more about the PluggableDevice API of TensorFlow from Using the PluggableDevice API of Tensorflow, Apple is able to translate TensorFlow operations to code that the GPU of the Apple Silicon (M1 and M2) can understand.

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