So I got a 'day one' m1 Macbook Air (i.e. an Apple Silicon ARM Mac) and I had to do some work to spin up my Data Science environment... and do it well. I'm going to leave my notes here in the hopes it (1) helps me not lose these and (2) helps someone out on the internet.
Rosetta is a way to emulate Intel/x86 Software on your ARM processor. There are two ways to use Rosetta in your terminal
- Add
arch -x86_64before your terminal commands OR - Just create a terminal that always launches in Rosetta. Remember once you did this, you'll effectively have two terminals -- one Rosetta and one ARM. Also as of 06/2021, you may not find you need to do this anymore in light of how many programs 'just work' on ARM now.
Once you do that, you can install Homebrew using Rosetta and get access to most of its packages.
Note that not everything compiles in Rosetta and you should be wary of it. I have not tested the world of software but of note, Docker is one of the most prominent examples that will not work with Rosetta. Scroll down to the very last section to see the list of all programs that won't work at all.
Homebrew now has native support for Apple Silicon as of Version 3.0 and you can follow the default installation at https://brew.sh. This'll install to /opt/homebrew/bin/brew. Bonus: The ARM native version now installs the Apple Command Line Tools for you for free.
Even if Apple Command Line Tools + Homebrew comes with Python, and they do, I still recommend going out of your way to install Miniforge below to get the complete Conda ecosystem.
So normally I'd use Anaconda but Anaconda is only optimized for Intel Macs at the time this guide was written (and still is as of 06/2021). So I used Miniforge which fortunately has an ARM equivilant and basically wraps Conda + Python in a happy ARM architecture.
To install Miniforge, use the installer link at https://github.com/conda-forge/miniforge#miniforge3 and then install it in your normal terminal. Remember not to run this in a Rosetta Terminal, if you created one, because this installer script can run in your regular ARM terminal.
After you do this, to ensure you truly got an ARM version of Python, run file $(which python) in your terminal. It should point to your MiniForge version and say it's an ARM executable. For me, it said: /Users/YOURUSERNAMEHERE/miniforge3/bin/python: Mach-O 64-bit executable
That said even though it has Conda, that doesn't mean all packages are ARM ready -- only some are. The quick way to see what packages may be available for ARM are if you:
- See a package made by conda-forge (https://anaconda.org/conda-forge) AND
- That package has an architecture of
osx-arm64or the package isnoarchand all its dependencies are built.
If your package meets both requirements, you can install it by running conda -c conda-forge <PACKAGE> once you have Miniforge installed.
Note: This doesn't mean that these packages are all ARM 'optimized' to use all of the fancy GPUs and Neural Cores. It just means that unlike Intel compiled packages, these packages won't be emulated, so they can run on the CPU natively. That said, take all of these with a grain of salt, I haven't got the chance to put all of these packages through their paces so some functionality might still be broken.
The biggest problem with ARM versions are that these are often behind official releases, and are less frequently updated. Before building your local environment in a full ARM mode, please check requirements of your project.
Most commonly used libraries:
- Arrow
- Bokeh (Added on 11/23/2020)
- Cython BLIS
- JupyterLab & Notebooks: The latest version of
appnope(updated around 11/30/2020) has fixed a Ctypes bug on ARM, which now permits Jupyter to run on ARM. - LightGBM
- Matplotlib
- NetworkX
- NLTK
- Numpy
- Pandas
- PyTorch with Torchvision (Added on 03/08/2021)
- Scikit-Learn (Added on 11/21/2020)
- Scipy
- Spacy
- Statsmodel (Added on 11/21/2020)
- Streamlit
- TensorFlow, official version with Metal plugin (Added on 04/07/2021)
- R language (
conda install r-basefor a minimal R installation orconda install rfor R plus recommended packages) - Fortran compiler (
conda install gfortran)
- Gensim: No support, no visibility on ETA
- Keras: Just no.
- MKL
- Torchaudio or Torchtext: support packages for PyTorch, both Torchaudio or Torchtext cannot be installed in an ARM version of Python
- Umap-learn
- XGBoost: no official conda ARM version available, but can be installed from pip sources
If you must use Anaconda because a package isn't ARM ready, you can use Rosetta to emulate an Intel Anaconda as per the tips in the Homebrew Section. You can then use said terminal to install Anaconda. Remember that just because you can emulate Anaconda doesn't mean you will be able to successfully run every package.
Many packages, including Apple's TensorFlow, prefers Python 3.8 over the Python 3.9 that Miniconda comes with. Remember that is still an option with Miniforge, you can downgrade by creating a new environment using something like this: conda create --name python38 python=3.8.
Each recipe for Python Packages appear to flip-flop between wanting Conda version 4.9.0 or something newer. If you have to jump around to make things work, remember the command is conda install conda=PUT_VERSION_HERE.
- MATLAB: The latest version can run under Rosetta, with a few exceptions for some packages. An ARM Version is in development but there's no release date as of yet.
As of 04/2021, Docker & Visual Studio Code is ARM native.