Installing scipy and scikit-learn on apple m1

Question

The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:

ERROR: Failed building wheel for numpy

Failed to build numpy

ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly

Why should Numpy be build again when I have the latest version from pip already installed?

Every previous installation was done using python3.9 -m pip install ... on Mac OS 11.3.1 with the apple m1 chip.

Maybe somebody knows how to deal with this error or if its just a matter of time.

Answer 1

UPDATE: scikit-learn now works via pip ✅

Just first brew install openblas - it has instructions for different processors (wikipedia)

brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Worked great on M1 Pro 🎉

Extra details

Pip downloaded the source from Pipy, then built the wheel targeting MacOS X 12.0, and arm64 (apple silicon): scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl.

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Note on Pipy: we usually download either a pre-built wheel (yay, this is excellent for reliable distribution and ensuring compatability). Or, if no prebuilt wheel exists (sad) then we download a tar.gz and build it ourselves. This happens because the authors don't publish a prebuilt wheel to Pipy, but more and more people are adding this to their CI (github actions) workflow. Building the wheel ourselves takes more cpu time, and is generally less reliable but works in this case.

Here we are downloading a pre-built wheel that has very few limitations: it works for any version of python 3, for any os, for any architecture (like amd64 or arm64): click-8.0.3-py3-none-any.whl

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Here apparently we had no wheel available, so we have to build it ourselves with setuptools running setup.py.

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done

Good luck and happy piping.

Answer 2

Finally managed to sort this using below steps:

1. install python 3.9 using brew
 (brew install [email protected])
2. setup PATH to pick brew python3 first (export PATH=opt/homebrew/bin:$PATH)
3. Run below commands to install scipy and scikit-learn

    >> /opt/homebrew/bin/brew install openblas
    >> export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
    >> export CFLAGS="-falign-functions=8 ${CFLAGS}"
    >> git clone https://github.com/scipy/scipy.git
    >> cd scipy
    >> git submodule update --init
    >> /opt/homebrew/bin/pip3 install .
    >> /opt/homebrew/bin/pip3 install scikit-learn

4. go to the folder where virtual env needs to be created and run python3 -m venv --system-site-packages .venv

Answer 3

As of March 14 (pie day!) 2022, all I had to do was type the following in a conda environment:

conda install --channel=conda-forge scikit-learn

That was it. I am running a MacBook Pro (13", M1, 2020) with MacOS 11.6, and python 3.8 in the conda environment.

Answer 4

Please see this note of scikit-learn about

Installing on Apple Silicon M1 hardware

The recently introduced macos/arm64 platform (sometimes also known as macos/aarch64) requires the open source community to upgrade the build configuation and automation to properly support it.

At the time of writing (January 2021), the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:

https://github.com/conda-forge/miniforge

The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:

https://github.com/scikit-learn/scikit-learn/issues/19137