Package or source wheel instead of the platform-specific wheels. Prefer using the system-provided libsndfile, install the source The packaged libsndfile in the platform-specific wheels is triedīefore falling back to any system-provided libsndfile. In 0.12.0, we changed the load order of the libsndfile library. The oldĬtype arguments still work, but are now officially deprecated. Methods to dtype, using the Numpy dtype notation. In 0.9.0, we changed the ctype arguments of the buffer_* Also, the order of arguments of the write functionĬhanged from write(data, file. In 0.8.0, we changed the default value of always_2d from True Inconsistencies, particularly in the the ordering and naming ofįunction arguments and the removal of the indexing interface. Notably, we changed the import name from import pysoundfile to

| sec | np_veclib | np_default | np_openblas | np_netlib | np_openblas_source | M1 | i9–9880H | i5-6360U |

dario.py: A benchmark script by Dario Radečić at the post above.ģ.

It's said that, numpy installed in this way is optimized for Apple M1 and will be faster.

Apple-TensorFlow: with python installed by miniforge, I directly install tensorflow, and numpy will also be installed.

conda install numpy: numpy from original conda-forge channel, or pre-installed with anaconda.

(Check from Activity Monitor, Kind of python process is Intel).

Anaconda.: Then python is run via Rosseta.

(Check from Activity Monitor, Kind of python process is Apple).

Miniforge-arm64, so that python is natively run on M1 Max Chip.

On M1 Max, why run in P圜harm IDE is constantly slower ~20% than run from terminal, which doesn't happen on my old Intel Mac.Įvidence supporting my questions is as follows:.

On M1 Max and native run, why there isn't significant speed difference between conda installed Numpy and TensorFlow installed Numpy - which is supposed to be faster?.

On M1 Max, why there isn't significant speed difference between native run (by miniforge) and run via Rosetta (by anaconda) - which is supposed to be slower ~20%?.

Why python run natively on M1 Max is greatly (~100%) slower than on my old MacBook Pro 2016 with Intel i5?.

I've tried several combinational settings to test speed - now I'm quite confused. I just got my new MacBook Pro with M1 Max chip and am setting up Python.

Download pycharm mac m1