Tips of common data tools on M1 Macbook Air
Long story short, the M1 Macbook Air is so sweet and it is friendly to data modeling work with
First, homebrew and miniforge
The easiest start comes from
Homebrew by following the official installation at https://brew.sh/
The next step is
conda via https://github.com/conda-forge/miniforge. Please make sure to choose
Miniforge3-MacOSX-arm64. It runs like a charm.
xgboost comes with native M1 support and the best practice as tody is compiling via
pip with the
clang. Some missing libs can be either install via
brew or use
brew install cmake conda create -n xgboost_env python=3.9 conda install numpy scipy pip install xgboost
scipy can bring in
llvm-openmp-12.0.1 which is the trick.
neo4j runs fine on M1 and one can download it from https://neo4j.com/download-center/. I am not sure if it is M1 native, but seems no problem. The only pitfall comes from the
java version and I have this best practice to install Java 11 https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jdk
By any chance one needs to uninstall another java runtime to resolve the conflict, please follow https://docs.oracle.com/en/java/javase/16/install/installation-jdk-macos.html
pytorch has no surprise of no native M1 support as today, so the official installation works fine.
tensorflow has some luck from Apple. By following this https://developer.apple.com/metal/tensorflow-plugin/, one can use the M1 chip’s GPU via
Metal. The benchmark speed is not bad.