Ich installierte erfolgreich Numpy "numpy-1.12.0.dev0 + 1380fdd-py2.7-linux-x86_64.egg" von der Quelle mit Intel MKL (folgend hauptsächlich der Anweisung von https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl). numpy.show_config() zeigt folgendes:Kann Scipy mit Intel MKL nicht installieren
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']
Auch numpy.test() fein funktioniert:
>>> 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>
Aber aus irgendeinem Grund bin ich nicht in der Lage Scipy auch von der Quelle über python setup.py config --compiler=intelem --fcompiler=intelem build_clib --compiler=intelem --fcompiler=intelem build_ext --compiler=intelem --fcompiler=intelem install noch über pip install scipy zu installieren. Ich erhalte den folgenden Fehler aus Quelle:
RuntimeError: Running cythonize failed!
für cython Überprüfung:
cython -V
Cython version 0.23
Installieren Sie es über pip führt zu:
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/
Jede Idee, was ich falsch mache?
Mein Betriebssystem ist Fedora 23 auf einem Thinkpad T450s. Eine Nebenfrage ist, dass ich auch anmerke, dass numpy.test() viel schneller ist, ohne das Intel MKL zu verwenden. Irgendeine Erklärung dafür?
Vielen Dank.