自分でも驚くくらい凡ミスで悩んでいたためメモ。 mysqlを起動させようとスタートすると、エラー文字。 ERROR 2002 (HY000): Can't connect to local MySQL server through socket '/tmp/mysql.sock' (2) とりあえず、/tmpディレクトリ配下にmysql.sockを作成。 cd /tmp/ touch mysql.sock アクセス権限をオールクリア chmod 777 mysql.sock すると()の中が2から38に変わった。 ERROR 2002 (HY000): Can't connect to local MySQL server through socket '/tmp/mysql.sock' (38) しかし、よく似た記事を発見。 その記事では、 mysql.server start を飛ばしていたとのこと。 自分のコマンドを見直すと、 mysql　server.start になっていた。 mysqlでERROR 2002 (HY000)のエラーが出たら、 もしかしたらいつもと違うことをやっているだけの可能性がある ということを疑っても良いかもしれない。

※この記事は，線形演算的に快適なM1 native Python3.9環境が欲しい方が対象です。 はじめに 最近M1 Macを買って一応brew install pythonとpip install numpyでinstallまではしたけど，Apple Siliconなのにnumpyだけやたら遅いな？と感じていませんか…？それはきっとOpenBLASがlinkされていないからです。この機会に以下の方法でnumpyにOpenBLASをlinkして，最大300倍の高速化チャンス！を獲得しましょう。 前提 本記事では以下を仮定しています M1 Mac Arm版のHomebrew Arm版のPython3.9 インストール方法 numpy/scipyをOpenBLASにlinkしてinstallするコマンドです # numpy/scipyに必要 % brew install openblas gfortran % pip3 install cython pybind11 # おまじない % export OPENBLAS="$(brew --prefix openblas)/lib/" # build from source % pip3 install --no-binary :all: --no-use-pep517 numpy # おまけでscipyも（結構長いので注意） % pip3 install --no-binary :all: --no-use-pep517 scipy 検証 以下を実行してbenchmarkを計測しました ▼ https://gist.github.com/markus-beuckelmann/8bc25531b11158431a5b09a45abd6276 Name Python Platform BLAS / LAPACK 行列積 (4096x4096) [sec] ドット積 (1x524228) [ms] SVD (2048x1024) [sec] Cholesky分解 (2048x2048) [sec] 対角化 (2048x2048) [sec] Pure NumPy aarch64 (Homebrew) - 298.54 1.09 13.27 2.12 73.81 NumPy + OpenBLAS aarch64 (Homebrew) OpenBLAS 0.95 0.28 2.49 0.11 10.27 NumPy + Intel MKL intel (Miniconda) Intel MKL 2.53 0.08 0.96 0.22 8.16 NativeなArm版OpenBLASがエミュレートされているRosetta 2 + Intel MKLに行列積以外で負けているのは何故なんでしょう？AppleのRosetta 2がすごいのかそれもと数学ライブラリの開発をサボっているからなのかわかりません。そしてPure NumPyは遅すぎて途中で計測を辞めたくなりました。 検証ログ Pure NumPy (aarch64) Dotted two 4096x4096 matrices in 298.54 s. Dotted two vectors of length 524288 in 1.09 ms. SVD of a 2048x1024 matrix in 13.27 s. Cholesky decomposition of a 2048x2048 matrix in 2.12 s. Eigendecomposition of a 2048x2048 matrix in 73.81 s. This was obtained using the following Numpy configuration: blas_mkl_info: NOT AVAILABLE blis_info: NOT AVAILABLE openblas_info: NOT AVAILABLE atlas_3_10_blas_threads_info: NOT AVAILABLE atlas_3_10_blas_info: NOT AVAILABLE atlas_blas_threads_info: NOT AVAILABLE atlas_blas_info: NOT AVAILABLE blas_info: NOT AVAILABLE blas_src_info: NOT AVAILABLE blas_opt_info: NOT AVAILABLE lapack_mkl_info: NOT AVAILABLE openblas_lapack_info: NOT AVAILABLE openblas_clapack_info: NOT AVAILABLE flame_info: NOT AVAILABLE atlas_3_10_threads_info: NOT AVAILABLE atlas_3_10_info: NOT AVAILABLE atlas_threads_info: NOT AVAILABLE atlas_info: NOT AVAILABLE lapack_info: NOT AVAILABLE lapack_src_info: NOT AVAILABLE lapack_opt_info: NOT AVAILABLE numpy_linalg_lapack_lite: language = c define_macros = [('HAVE_BLAS_ILP64', None), ('BLAS_SYMBOL_SUFFIX', '64_')] NumPy w/ OpenBLAS (aarch64) qiita@m1 ~ % python numpy_benchmark.py Dotted two 4096x4096 matrices in 0.95 s. Dotted two vectors of length 524288 in 0.28 ms. SVD of a 2048x1024 matrix in 2.49 s. Cholesky decomposition of a 2048x2048 matrix in 0.11 s. Eigendecomposition of a 2048x2048 matrix in 10.27 s. This was obtained using the following Numpy configuration: blas_mkl_info: NOT AVAILABLE blis_info: NOT AVAILABLE openblas_info: libraries = ['openblas', 'openblas'] library_dirs = ['/opt/homebrew/opt/openblas/lib/'] language = c define_macros = [('HAVE_CBLAS', None)] runtime_library_dirs = ['/opt/homebrew/opt/openblas/lib/'] blas_opt_info: libraries = ['openblas', 'openblas'] library_dirs = ['/opt/homebrew/opt/openblas/lib/'] language = c define_macros = [('HAVE_CBLAS', None)] runtime_library_dirs = ['/opt/homebrew/opt/openblas/lib/'] lapack_mkl_info: NOT AVAILABLE openblas_lapack_info: libraries = ['openblas', 'openblas'] library_dirs = ['/opt/homebrew/opt/openblas/lib/'] language = c define_macros = [('HAVE_CBLAS', None)] runtime_library_dirs = ['/opt/homebrew/opt/openblas/lib/'] lapack_opt_info: libraries = ['openblas', 'openblas'] library_dirs = ['/opt/homebrew/opt/openblas/lib/'] language = c define_macros = [('HAVE_CBLAS', None)] runtime_library_dirs = ['/opt/homebrew/opt/openblas/lib/'] NumPy w/ Intel MKL (x86_64) Dotted two 4096x4096 matrices in 2.53 s. Dotted two vectors of length 524288 in 0.08 ms. SVD of a 2048x1024 matrix in 0.96 s. Cholesky decomposition of a 2048x2048 matrix in 0.22 s. Eigendecomposition of a 2048x2048 matrix in 8.16 s. This was obtained using the following Numpy configuration: blas_mkl_info: libraries = ['mkl_rt', 'pthread'] library_dirs = ['/Users/qiita/miniconda3/lib'] define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)] include_dirs = ['/Users/user/miniconda3/include'] blas_opt_info: libraries = ['mkl_rt', 'pthread'] library_dirs = ['/Users/qiita/miniconda3/lib'] define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)] include_dirs = ['/Users/user/miniconda3/include'] lapack_mkl_info: libraries = ['mkl_rt', 'pthread'] library_dirs = ['/Users/qiita/miniconda3/lib'] define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)] include_dirs = ['/Users/user/miniconda3/include'] lapack_opt_info: libraries = ['mkl_rt', 'pthread'] library_dirs = ['/Users/qiita/miniconda3/lib'] define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)] include_dirs = ['/Users/qiita/miniconda3/include']