![]() ![]() # Option 2: brew openblas + local compileĬholesky decomposition of a 2048x2048 matrix in 0.08 s.Įigendecomposition of a 2048x2048 matrix in 8.41 s. # -ĭotted two vectors of length 524288 in 0.25 ms.Ĭholesky decomposition of a 2048x2048 matrix in 0.09 s.Įigendecomposition of a 2048x2048 matrix in 7.20 s. I got these numbers: # Option 1: with the 'included' openblas from conda install Just as a curiosity I managed to compile numpy against a copy of openblasdownloaded with Homebrew and compared the performance of the default install (the simply conda install numpy) to the alternative (Numpy source code + brewed openblas) The only difference is that I am using miniconda instead of miniforge (which at this date should be OK ?) I tried to get numpy to link to Accelerate following the steps here but when I checked with np.show_config it was showing me open blas! | 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.My old i5-6360U 2cores on MacBook Pro 2016 13in.Then add path /opt/homebrew/opt/openblas to site.cfg and build Numpy from source. np_openblas_source: First install openblas by brew install openblas. Note that mkl or blis is not supported on arm64. To see the difference, examine by conda list - e.g. numpy.show_config() will show identical results. The above ABC options are directly installed from conda-forge channel. np_netlib: conda create -n np_netlib python=3.9 numpy blas=*=*netlib* np_openblas: conda create -n np_openblas python=3.9 numpy blas=*=*openblas* np_default: conda create -n np_default python=3.9 numpy Competitors:Įxcept for the above optimal one, I also tried several other installations But the new installed one is from conda-forge channel and is slow.Ĭomparisons to other installations: 1. conda install pandas, then numpy will be in The following packages will be installed list and installed again. Make conda recognize packages installed by pip conda config -set pip_interop_enabled true For further installing other packages using conda ![]() ![]() Rsync -av testenv/lib/python3.9/site-packages/shiboken6* $MY_PROJECT_VENV_PATH/lib/python3.Then, info like /System/Library/Frameworks/amework/Headers should be printed. Rsync -av testenv/lib/python3.9/site-packages/PySide6* $MY_PROJECT_VENV_PATH/lib/python3.9/site-packages/ Rsync -av testenv/bin/shiboken6* $MY_PROJECT_VENV_PATH/bin You will see the PySide package in pip list: pip list | grep PySideĪlso I use these commandd to copy the installed package to another project export MY_PROJECT_VENV_PATH=./another-project/venv To install the PySide into the current virtual env run this: python3 setup.py install -qmake=/opt/homebrew/Cellar/qt/6.1.2/bin/qmake -build-tests -ignore-git -parallel=8 -reuse-buildĬompleted. In the result I see the finish message: - Build completed (413s) It takes a while time (check the correct path for qmake - qmake -v): python setup.py build -qmake=/opt/homebrew/Cellar/qt/6.1.2/bin/qmake -build-tests -ignore-git -parallel=8 I've found that the installation will work only if set this system variable: export CLANG_INSTALL_DIR=/opt/homebrew/opt/llvm In the begin I start install necessary utilities via brew: brew install llvm cmake ninjaĪfter that I clone the setup repo, activate python venv and install python packages: git clone -recursive Hi use this article and I successfully build PySide6 - 6.1.2 from sources on my MacBook Pro with M1. ![]()
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