我成功地从源代码中使用Intel MKL成功安装了Numpy“ numpy-1.12.0.dev0 + 1380fdd-py2.7-linux-x86_64.egg”（主要遵循https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl中的说明）。 numpy.show_config()显示以下内容：
Python 2.7.10 (default, Sep 8 2015, 17:20:17)
[GCC 5.1.1 20150618 (Red Hat 5.1.1-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.show_config()
lapack_opt_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/lib/intel64']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/include']
blas_opt_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/lib/intel64']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/include']
lapack_mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/lib/intel64']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/include']
blas_mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/lib/intel64']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/include']
mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/lib/intel64']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/opt/intel/compilers_and_libraries_2016.1.150/linux/mkl/include']
numpy.test()也可以正常工作：>>> numpy.test()
Running unit tests for numpy
NumPy version 1.12.0.dev0+1380fdd
NumPy relaxed strides checking option: True
NumPy is installed in /usr/lib64/python2.7/site-packages/numpy-1.12.0.dev0+1380fdd-py2.7-linux-x86_64.egg/numpy
Python version 2.7.10 (default, Sep 8 2015, 17:20:17) [GCC 5.1.1 20150618 (Red Hat 5.1.1-4)]
nose version 1.3.7
[....................SKIP..........................]
----------------------------------------------------------------------
Ran 5855 tests in 51.180s
OK (KNOWNFAIL=6, SKIP=8)
<nose.result.TextTestResult run=5855 errors=0 failures=0>
但是由于某些原因，我什至无法通过
python setup.py config --compiler=intelem --fcompiler=intelem build_clib --compiler=intelem --fcompiler=intelem build_ext --compiler=intelem --fcompiler=intelem install或通过pip install scipy从源代码安装Scipy。从源头我收到以下错误：RuntimeError: Running cythonize failed!
检查cython：
cython -V
Cython version 0.23
通过点子引线将其安装到：
Command "/usr/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-ticToS/scipy/setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /tmp/pip-qnZ8HE-record/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-build-ticToS/scipy/
知道我在做什么错吗？
我的操作系统是Thinkpad T450s上的Fedora 23。附带的一个问题是，我也认识到不使用英特尔MKL，
numpy.test()的速度要快得多。有什么解释吗？非常感谢你。
最佳答案
通过groupinstall安装redhat-rpm-config，'Development Tools'解决了该问题。