python - 如何检查 NumPy 和 SciPy 中的 BLAS/LAPACK 链接？

Question

我正在构建基于 blas 和 lapack 的 numpy/scipy 环境，或多或少基于此演练。

完成后，如何检查我的 numpy/scipy 函数是否确实使用了先前构建的 blas/lapack 功能？

Answer 1

该方法numpy.show_config()（或numpy.__config__.show()）输出有关在构建时收集的链接的信息。我的输出看起来像这样。我认为这意味着我正在使用 Mac OS 附带的 BLAS/LAPACK。

>>> import numpy as np
>>> np.show_config()

lapack_opt_info:
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    extra_compile_args = ['-msse3']
    define_macros = [('NO_ATLAS_INFO', 3)]
blas_opt_info:
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    extra_compile_args = ['-msse3', '-I/System/Library/Frameworks/vecLib.framework/Headers']
    define_macros = [('NO_ATLAS_INFO', 3)]

Answer 2

您正在搜索的是： 系统信息

我用 atlas 编译了 numpy/scipy，我可以通过以下方式检查：

import numpy.distutils.system_info as sysinfo
sysinfo.get_info('atlas')

查看文档以获取更多命令。

Answer 3

您可以使用链接加载器依赖工具查看构建的 C 级挂钩组件，并查看它们是否对您选择的 blas 和 lapack 有外部依赖。我现在不在 linux 机器附近，但是在 OS X 机器上，您可以在包含安装的 site-packages 目录中执行此操作：

$ otool -L numpy/core/_dotblas.so 
numpy/core/_dotblas.so:
    /System/Library/Frameworks/Accelerate.framework/Versions/A/Accelerate (compatibility version 1.0.0, current version 4.0.0)
    /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 125.2.0)
    /System/Library/Frameworks/vecLib.framework/Versions/A/vecLib (compatibility version 1.0.0, current version 268.0.1)

$ otool -L scipy/linalg/flapack.so 
scipy/linalg/flapack.so (architecture i386):
    /System/Library/Frameworks/Accelerate.framework/Versions/A/Accelerate (compatibility version 1.0.0, current version 4.0.0)
    /usr/local/lib/libgcc_s.1.dylib (compatibility version 1.0.0, current version 1.0.0)
    /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 111.1.4)
    /System/Library/Frameworks/vecLib.framework/Versions/A/vecLib (compatibility version 1.0.0, current version 242.0.0)
scipy/linalg/flapack.so (architecture ppc):
    /System/Library/Frameworks/Accelerate.framework/Versions/A/Accelerate (compatibility version 1.0.0, current version 4.0.0)
    /usr/local/lib/libgcc_s.1.dylib (compatibility version 1.0.0, current version 1.0.0)
    /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 111.1.4)

$ otool -L scipy/linalg/fblas.so 
scipy/linalg/fblas.so (architecture i386):
    /System/Library/Frameworks/Accelerate.framework/Versions/A/Accelerate (compatibility version 1.0.0, current version 4.0.0)
    /usr/local/lib/libgcc_s.1.dylib (compatibility version 1.0.0, current version 1.0.0)
    /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 111.1.4)
    /System/Library/Frameworks/vecLib.framework/Versions/A/vecLib (compatibility version 1.0.0, current version 242.0.0)
scipy/linalg/fblas.so (architecture ppc):
    /System/Library/Frameworks/Accelerate.framework/Versions/A/Accelerate (compatibility version 1.0.0, current version 4.0.0)
    /usr/local/lib/libgcc_s.1.dylib (compatibility version 1.0.0, current version 1.0.0)
    /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 111.1.4)

代替gnu/Linux 系统ldd，otool你应该得到你需要的答案。

Answer 4

您可以使用以下方式显示 BLAS、LAPACK、MKL 链接show_config()：

import numpy as np
np.show_config()

这对我来说给出了输出：

mkl_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/my/environment/path/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/my/environment/path/include']
blas_mkl_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/my/environment/path/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/my/environment/path/include']
blas_opt_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/my/environment/path/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/my/environment/path/include']
lapack_mkl_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/my/environment/path/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/my/environment/path/include']
lapack_opt_info:
    libraries = ['mkl_rt', 'pthread']
    library_dirs = ['/my/environment/path/lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['/my/environment/path/include']

Answer 5

如果您安装了 anaconda-navigator（在 www.anaconda.com/anaconda/install/ 上适用于 linux、Windows 或 macOS） - blas、scipy 和 numpy 都将被安装，您可以通过单击导航器主页左侧的环境选项卡来查看它们页面（按 alpha 顺序查找每个目录）。安装完整的 anaconda（相对于 miniconda 或单个软件包）将负责安装数据科学所需的许多基本软件包。