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Opencv python mac m1 install#
| mysvd | 1.02300 | 4.29386 | 4.13854 | 4.75812 | 12.57879 | / | / | 2. Setting up Python with Miniforge - Step 1: Install Homebrew - Step 2: Install miniforge - Step 3: Setup and activate a virtual environment - Step 4: Install any Python library Option 1: Setting up Python and Data Science Packages with Anaconda. | 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). (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.