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Install NumPy on M1 Max

How to install numpy on M1 Max, with the most accelerated performance (Apple's vecLib)? Here's the answer as of Dec 6 2021.


I. Install miniforge

So that your Python is run natively on arm64, not translated via Rosseta.

  1. Download, then
  2. Run the script, then open another shell
$ bash
  1. Create an environment (here I use name np_veclib)
$ conda create -n np_veclib python=3.9
$ conda activate np_veclib

II. Install Numpy with BLAS interface specified as vecLib

  1. To compile numpy, first need to install cython and pybind11:
$ conda install cython pybind11
  1. Compile numpy by (Thanks @Marijn's answer) - don't use conda install!
$ pip install --no-binary :all: --no-use-pep517 numpy
  1. An alternative of 2. is to build from source
$ git clone
$ cd numpy
$ cp site.cfg.example site.cfg
$ nano site.cfg

Edit the copied site.cfg: add the following lines:

libraries = Accelerate, vecLib

Then build and install:

$ NPY_LAPACK_ORDER=accelerate python build
$ python install
  1. After either 2 or 3, now test whether numpy is using vecLib:
>>> import numpy
>>> numpy.show_config()

Then, info like /System/Library/Frameworks/vecLib.framework/Headers should be printed.

III. For further installing other packages using conda

Make conda recognize packages installed by pip

conda config --set pip_interop_enabled true

This must be done, otherwise if e.g. conda install pandas, then numpy will be in The following packages will be installed list and installed again. But the new installed one is from conda-forge channel and is slow.

Comparisons to other installations:

1. Competitors:

Except for the above optimal one, I also tried several other installations

  • A. np_default: conda create -n np_default python=3.9 numpy
  • B. np_openblas: conda create -n np_openblas python=3.9 numpy blas=*=*openblas*
  • C. np_netlib: conda create -n np_netlib python=3.9 numpy blas=*=*netlib*

The above ABC options are directly installed from conda-forge channel. numpy.show_config() will show identical results. To see the difference, examine by conda list - e.g. openblas packages are installed in B. Note that mkl or blis is not supported on arm64.

  • D. np_openblas_source: First install openblas by brew install openblas. Then add [openblas] path /opt/homebrew/opt/openblas to site.cfg and build Numpy from source.
  • M1 and i9–9880H in this post.
  • My old i5-6360U 2cores on MacBook Pro 2016 13in.

2. Benchmarks:

Here I use two benchmarks:

  1. My SVD decomposition
import time
import numpy as np
a = np.random.uniform(size=(300, 300))
runtimes = 10

timecosts = []
for _ in range(runtimes):
    s_time = time.time()
    for i in range(100):
        a += 1
    timecosts.append(time.time() - s_time)

print(f'mean of {runtimes} runs: {np.mean(timecosts):.5f}s')
  1. A benchmark script by Dario Radečić at the post above.

3. Results:

|  sec  | np_veclib | np_default | np_openblas | np_netlib | np_openblas_source | M1 | i9–9880H | i5-6360U |
| mysvd |  1.02300  |   4.29386  |   4.13854   |  4.75812  |      12.57879      |  / |     /    |  2.39917 |
| dario |     21    |     41     |      39     |    323    |         40         | 33 |    23    |    78    |
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placeless commented Apr 4, 2022

micromamba install numpy "libblas=*=*accelerate" works well.

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AlphaMoury commented Apr 5, 2022

Does this work on Mac M1 previous to Max/Pro?

I got an error exit status 1.

If not, is there something I could do to make it work on the Mac classic M1?

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AlphaMoury commented Apr 5, 2022

I made it work once, but then when I tried to reproduce, it failed. Any way to make it work consistently?

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placeless commented Apr 6, 2022

@AlphaMoury I don't think it has anything to do with the m1 processor version. I only did three things, hope this helps you:

  1. edit ~/.mambarc
  - conda-forge
  1. install micromamba arm64
curl -Ls | tar -xvj bin/micromamba

mv bin/micromamba /usr/local/bin/micromamba

/usr/local/bin/micromamba shell init -s zsh -p ~/micromamba

source ~/.zshrc

micromamba activate

micromamba install python=3.10 jupyter
  1. install numpy
micromamba install numpy "libblas=*=*accelerate"

Then run the benchmark tests above. The results are exactly as expected.

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