Let’s create a new environment, called tensorflow_m1: $ conda create -name tensorflow_m1 python=3.9 $ conda activate tensorflow_m1
Check this article to learn how to manage effectively many conda distributions simultaneously! Step 3: Setup Environment and Install Tensorflow base and tensorflow-metal plugin For those familiar with the conda ecosystem, only one conda distro can be “functional” at a given time. Anaconda or MiniConda, there is no need to uninstall it in order to use MiniForge.
If you already have a pre-existing conda distribution, e.g. Installation is easy: $ bash Miniforge3-MacOSX-arm64.sh
To download it, simply go to this page and download the installer for Apple Silicon. MiniForge is a minimalistic conda installer which uses by default the conda-forge channel and supports, among others, the aarch64 architecture (including Apple M1). Additionally, install the Command Line Tools: $ xcode-select -install Step 2: Install MiniForge The first component to install is Xcode, which can easily be downloaded from the App Store. This article discusses how to install Tensorflow on Miniforge by using the Metal plugin, a process that is more straightforward and less prone to errors. Unfortunately, that was not always the case. The process involved downloading, among other packages, a pre-configured environment.yml file with specific dependencies in such a way that no dependency conflicts will arise.
When Apple with M1 was released, the integration with Tensorflow was very difficult. The chip uses Apple Neural Engine, a component that allows Mac to perform machine learning tasks blazingly fast and without thermal issues. Since Apple abandoned Nvidia support, the advent of the M1 chip sparked new hope in the ML community.