Hi Bjorn,
IIUC, I think I’d try switching from openblas
to MKL. openblas
is what Numpy uses by default for algebra computations and has a long history of issues involving multiprocessing.
You can do conda list | grep blas
to see if you’re using openblas
in your virtual dev. The third column should tell which one it is:
libblas 3.8.0 16_openblas conda-forge
libcblas 3.8.0 16_openblas conda-forge
If your env uses openblas
, you can try MKL or blis
: conda install "libblas=*=*mkl" -c conda-forge
. You’ll probably need to install mkl
and mkl-service
too. Doc about these options is here.
After replacing blas
with MKL instead of openblas
, you should see:
libblas 3.8.0 14_mkl conda-forge
libcblas 3.8.0 14_mkl conda-forge
...
mkl 2019.5 281 conda-forge
mkl-service 2.3.0 py37h0b31af3_0 conda-forge
Maybe it’s not the whole issue, but this should help with computational speed