numpy

简介

NumPy是Python语言的一个扩充程序库。支持高阶大量的维度数组与矩阵运算，此外也针对数组运算提供大量的数学函数库

NumPy参考CPython(一个使用字节码的解释器)，而在这个Python实现解释器上所写的数学算法代码通常远比编译过的相同代码要来得慢。为了解决这个难题，NumPy引入了多维数组以及可以直接有效率地操作多维数组的函数与运算符。因此在NumPy上只要能被表示为针对数组或矩阵运算的算法，其运行效率几乎都可以与编译过的等效C语言代码一样快。[1]

numpy的依赖

独立安装

NumPy这个Python包本身不需依赖任何第三方库就能完成编译和安装使用，只不过其计算性能会受到影响。

在编译NumPy时通常会依赖一些经过特别优化的第三方科学计算库。

高性能NumPy包通常会依赖LAPACK和ATLAS库

lapack和atlas是科学计算领域的两个针对线性代数运算的经过特别优化的非常强大的工具库，

这两个库对于安装NumPy包来说并不是强制依赖的。

查看 numpy的配置

本机mac上

>>> import numpy as np
>>> np.show_config()
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_opt_info:
    extra_compile_args = ['-msse3', '-I/System/Library/Frameworks/vecLib.framework/Headers']
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
lapack_mkl_info:
  NOT AVAILABLE
openblas_lapack_info:
  NOT AVAILABLE
openblas_clapack_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_opt_info:
    extra_compile_args = ['-msse3']
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]

P100 GPU服务器ubuntu

>>> import numpy as np
>>> np.__config__.show()
mkl_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/home/song.xu01/anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/home/song.xu01/anaconda3/include']
blas_mkl_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/home/song.xu01/anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/home/song.xu01/anaconda3/include']
blas_opt_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/home/song.xu01/anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/home/song.xu01/anaconda3/include']
lapack_mkl_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/home/song.xu01/anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/home/song.xu01/anaconda3/include']
lapack_opt_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/home/song.xu01/anaconda3/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/home/song.xu01/anaconda3/include']

该服务器的全局python

>>> import numpy as np
>>> np.__config__.show()
lapack_opt_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    define_macros = [('HAVE_CBLAS', None)]
    language = c
blas_opt_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    define_macros = [('HAVE_CBLAS', None)]
    language = c
openblas_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    define_macros = [('HAVE_CBLAS', None)]
    language = c
blis_info:
  NOT AVAILABLE
openblas_lapack_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    define_macros = [('HAVE_CBLAS', None)]
    language = c
lapack_mkl_info:
  NOT AVAILABLE
blas_mkl_info:
  NOT AVAILABLE

安装numpy的过程，也会显示

扩展阅读

• 如何获得NumPy的最佳性能