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Description
It is now possible to install numpy, scipy... with a BLAS implementation that links to the macOS Accelerate library via conda-forge:
But at the moment, threadpoolctl does not recognize this BLAS implementation. I tested the following on a macOS/M1 machines:
mamba create -n accelerate numpy "libblas=*=*accelerate" threadpoolctl
mamba activate accelerate
python -m threadpoolctl -i numpy
[]
Output of mamba list:
# packages in environment at /Users/ogrisel/mambaforge/envs/accelerate:
#
# Name Version Build Channel
bzip2 1.0.8 h3422bc3_4 conda-forge
ca-certificates 2022.12.7 h4653dfc_0 conda-forge
libblas 3.9.0 16_osxarm64_accelerate conda-forge
libcblas 3.9.0 16_osxarm64_accelerate conda-forge
libcxx 15.0.7 h75e25f2_0 conda-forge
libffi 3.4.2 h3422bc3_5 conda-forge
libgfortran 5.0.0 12_2_0_hd922786_31 conda-forge
libgfortran5 12.2.0 h0eea778_31 conda-forge
liblapack 3.9.0 16_osxarm64_accelerate conda-forge
libsqlite 3.40.0 h76d750c_0 conda-forge
libzlib 1.2.13 h03a7124_4 conda-forge
llvm-openmp 16.0.0 h7cfbb63_0 conda-forge
ncurses 6.3 h07bb92c_1 conda-forge
numpy 1.24.2 py311h60f8152_0 conda-forge
openssl 3.1.0 h03a7124_0 conda-forge
pip 23.0.1 pyhd8ed1ab_0 conda-forge
python 3.11.0 h3ba56d0_1_cpython conda-forge
python_abi 3.11 3_cp311 conda-forge
readline 8.2 h92ec313_1 conda-forge
setuptools 67.6.0 pyhd8ed1ab_0 conda-forge
threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge
tk 8.6.12 he1e0b03_0 conda-forge
tzdata 2023b h71feb2d_0 conda-forge
wheel 0.40.0 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h57fd34a_0 conda-forge
Even if we cannot detect the number of threads it would be helpful to detect that numpy and/or scipy rely on Accelerate.
For CI purposes, we could probably leverage Cirrus CI to gain access to macOS/arm64 machines. Alternatively we could try with the standard macOS/x86_64 CI on Azure.
EDIT: GitHub Actions now also has macOS M1 runners by default.
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