What is the proper way to install TensorFlow on Apple M1 in 2022

Distilling the official directions from Apple (as of 13 July 2022), one would create an environment using the following YAML:

tf-metal-arm64.yaml

name: tf-metal
channels:
  - apple
  - conda-forge
dependencies:
  - python=3.9  ## specify desired version
  - pip
  - tensorflow-deps

  ## uncomment for use with Jupyter
  ## - ipykernel

  ## PyPI packages
  - pip:
    - tensorflow-macos
    - tensorflow-metal  ## optional, but recommended

Edit to include additional packages.

Creating environment

Before creating the environment we need to know what the base architecture is. Check this with conda config --show subdir.

Native (osx-arm64) base

If you have installed a native osx-arm64 Miniforge variant (I recommend Mambaforge), then you can create with:

mamba env create -n my_tf_env -f tf-metal-arm64.yaml

_{Note: If you don’t have Mamba, then substitute conda for mamba; or install it for much faster solving: conda install -n base mamba.}

Emulated (osx-64) base

If you do not have a native base, then you will need to override the subdir setting:

## create env
CONDA_SUBDIR=osx-arm64 mamba env create -n my_tf_env -f tf-metal-arm64.yaml

## activate
mamba activate my_tf_env

## permanently set the subdir
conda config --env --set subdir osx-arm64

Be sure to always activate the environment before installing or updating packages.