Issue 1:
Time for this release candidate's set of MKL failures. Do other people test using MKL, or should I try to make a habit of doing this check before each release?

icc --version is icc (ICC) 16.0.1 20151021

numpy on maintenance/1.10.x branch, version 1.10.0.dev0+075cc98, numpy.test('full') passing; scipy.test('full') gives:

>>> scipy.test('full')
Running unit tests for scipy
NumPy version 1.10.0.dev0+075cc98
NumPy relaxed strides checking option: False
NumPy is installed in /home/larsoner/.local/lib/python2.7/site-packages/numpy
SciPy version 0.17.0rc1
SciPy is installed in /home/larsoner/.local/lib/python2.7/site-packages/scipy
Python version 2.7.10 (default, Oct 14 2015, 16:09:02) [GCC 5.2.1 20151010]
nose version 1.3.6

...

======================================================================
FAIL: test_lorentz (test_odr.TestODR)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/larsoner/.local/lib/python2.7/site-packages/scipy/odr/tests/test_odr.py", line 293, in test_lorentz
3.7798193600109009e+00]),
File "/home/larsoner/.local/lib/python2.7/site-packages/numpy/testing/utils.py", line 892, in assert_array_almost_equal
precision=decimal)
File "/home/larsoner/.local/lib/python2.7/site-packages/numpy/testing/utils.py", line 713, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

(mismatch 100.0%)
x: array([ 1.000000e+03, 1.000000e-01, 3.800000e+00])
y: array([ 1.430678e+03, 1.339051e-01, 3.779819e+00])

======================================================================
FAIL: test_multi (test_odr.TestODR)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/larsoner/.local/lib/python2.7/site-packages/scipy/odr/tests/test_odr.py", line 190, in test_multi
0.5101147161764654, 0.5173902330489161]),
File "/home/larsoner/.local/lib/python2.7/site-packages/numpy/testing/utils.py", line 892, in assert_array_almost_equal
precision=decimal)
File "/home/larsoner/.local/lib/python2.7/site-packages/numpy/testing/utils.py", line 713, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

(mismatch 100.0%)
x: array([ 4. , 2. , 7. , 0.4, 0.5])
y: array([ 4.379988, 2.433306, 8.002885, 0.510115, 0.51739 ])

======================================================================
FAIL: test_pearson (test_odr.TestODR)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/larsoner/.local/lib/python2.7/site-packages/scipy/odr/tests/test_odr.py", line 236, in test_pearson
np.array([5.4767400299231674, -0.4796082367610305]),
File "/home/larsoner/.local/lib/python2.7/site-packages/numpy/testing/utils.py", line 892, in assert_array_almost_equal
precision=decimal)
File "/home/larsoner/.local/lib/python2.7/site-packages/numpy/testing/utils.py", line 713, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

(mismatch 100.0%)
x: array([ 1., 1.])
y: array([ 5.47674 , -0.479608])

----------------------------------------------------------------------
Ran 21565 tests in 1190.894s

FAILED (KNOWNFAIL=130, SKIP=1811, failures=3)
<nose.result.TextTestResult run=21565 errors=0 failures=3>
>>>

I can also try with latest numpy master, but I figured we should try to clear up the 1.10 failures to see if that gets everything working first.

Issue 2:
Hi,

I've been pulling my hair out for a few days trying to get a build of numpy/scipy against the MKL to pass all the scipy tests. I've ran out of ideas to try, so any ideas on what to try next would be greatly appreciated! My guess is that I'm missing an ifort flag, but I've tried a bunch of different combinations without improving the situation.

My system is running 64bit Ubuntu 13.04 with a Intel Core i7-4700MQ CPU with Python 2.7.6. I've been using icc and ifort versions 14.0.2, and MKL version 11.1.2. I've been building Numpy 1.8.0 and Scipy 0.13.3.

I've been pretty much following Intel's instructions on building numpy and scipy, http://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl .

My site.cfg looks like this:

[mkl]
library_dirs = /opt/intel/mkl/lib/intel64
include_dirs = /opt/intel/mkl/include
mkl_libs = mkl_rt
lapack_libs =

I modified self.cc_exe in the following class in numpy/distutils/intelccompiler.py:

class IntelEM64TCCompiler(UnixCCompiler):
""" A modified Intel x86_64 compiler compatible with a 64bit gcc built Python.
"""
compiler_type = 'intelem'
cc_exe = 'icc -m64 -fPIC'
cc_args = "-fPIC"
def __init__ (self, verbose=0, dry_run=0, force=0):
UnixCCompiler.__init__ (self, verbose, dry_run, force)
self.cc_exe = 'icc -m64 -O3 -g -fPIC -fp-model strict -fomit-frame-pointer -openmp -xhost'
compiler = self.cc_exe
self.set_executables(compiler=compiler,
compiler_so=compiler,
compiler_cxx=compiler,
linker_exe=compiler,
linker_so=compiler + ' -shared')

I left numpy/distutils/fcompiler/intel.py alone, the class for 64 bit compiler is here:

class IntelEM64TFCompiler(IntelFCompiler):
compiler_type = 'intelem'
compiler_aliases = ()
description = 'Intel Fortran Compiler for 64-bit apps'

version_match = intel_version_match('EM64T-based|Intel\\(R\\) 64|64|IA-64|64-bit')

possible_executables = ['ifort', 'efort', 'efc']

executables = {
'version_cmd' : None,
'compiler_f77' : [None, "-FI"],
'compiler_fix' : [None, "-FI"],
'compiler_f90' : [None],
'linker_so' : ['<F90>', "-shared"],
'archiver' : ["ar", "-cr"],
'ranlib' : ["ranlib"]
}

def get_flags(self):
return ['-fPIC']

def get_flags_opt(self):
#return ['-i8 -xhost -openmp -fp-model strict']
return ['-xhost -openmp -fp-model strict']

def get_flags_arch(self):
return []

I compiled and installed numpy as follows,

python setup.py config --compiler=intelem build_clib --compiler=intelem build_ext --compiler=intelem install

It built just fine and passed all the tests,

Ran 4969 tests in 29.835s

OK (KNOWNFAIL=5, SKIP=7)

I then built scipy as follows,

python setup.py config --compiler=intelem --fcompiler=intelem build_clib --compiler=intelem --fcompiler=intelem build_ext --compiler=intelem --fcompiler=intelem install

Which also built and installed just fine. However, I fail the following four tests:

======================================================================
FAIL: test_lorentz (test_odr.TestODR)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/scipy/odr/tests/test_odr.py", line 293, in test_lorentz
3.7798193600109009e+00]),
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/numpy/testing/utils.py", line 811, in assert_array_almost_equal
header=('Arrays are not almost equal to %d decimals' % decimal))
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

(mismatch 100.0%)
x: array([ 1.00000000e+03, 1.00000000e-01, 3.80000000e+00])
y: array([ 1.43067808e+03, 1.33905090e-01, 3.77981936e+00])

======================================================================
FAIL: test_multi (test_odr.TestODR)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/scipy/odr/tests/test_odr.py", line 190, in test_multi
0.5101147161764654, 0.5173902330489161]),
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/numpy/testing/utils.py", line 811, in assert_array_almost_equal
header=('Arrays are not almost equal to %d decimals' % decimal))
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

(mismatch 100.0%)
x: array([ 4. , 2. , 7. , 0.4, 0.5])
y: array([ 4.37998803, 2.43330576, 8.00288459, 0.51011472, 0.51739023])

======================================================================
FAIL: test_pearson (test_odr.TestODR)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/scipy/odr/tests/test_odr.py", line 236, in test_pearson
np.array([5.4767400299231674, -0.4796082367610305]),
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/numpy/testing/utils.py", line 811, in assert_array_almost_equal
header=('Arrays are not almost equal to %d decimals' % decimal))
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

(mismatch 100.0%)
x: array([ 1., 1.])
y: array([ 5.47674003, -0.47960824])

======================================================================
FAIL: test_iterative.test_convergence(<function bicgstab at 0x3b58938>, <nonsymposdef>)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/tests/test_iterative.py", line 194, in check_convergence
assert_equal(info,0)
File "/home/youngpm/pythonenv/mkl/lib/python2.7/site-packages/numpy/testing/utils.py", line 317, in assert_equal
raise AssertionError(msg)
AssertionError:
Items are not equal:
ACTUAL: -10
DESIRED: 0

----------------------------------------------------------------------
Ran 8934 tests in 59.121s

FAILED (KNOWNFAIL=115, SKIP=220, failures=4)

I can recreate the failure with the following script:

import numpy as np
from numpy import pi
from scipy.odr import Data, Model, ODR, RealData, odr_stop

def multi_fcn(B, x):
if (x < 0.0).any():
raise odr_stop
theta = pi*B[3]/2.
ctheta = np.cos(theta)
stheta = np.sin(theta)
omega = np.power(2.*pi*x*np.exp(-B[2]), B[3])
phi = np.arctan2((omega*stheta), (1.0 + omega*ctheta))
r = (B[0] - B[1]) * np.power(np.sqrt(np.power(1.0 + omega*ctheta, 2) +
np.power(omega*stheta, 2)), -B[4])
ret = np.vstack([B[1] + r*np.cos(B[4]*phi),
r*np.sin(B[4]*phi)])
return ret

if __name__ == "__main__":

multi_mod = Model(multi_fcn,
meta=dict(name='Sample Multi-Response Model',
ref='ODRPACK UG, pg. 56'),)
multi_x = np.array([30.0, 50.0, 70.0, 100.0, 150.0, 200.0, 300.0, 500.0,
700.0, 1000.0, 1500.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0,
15000.0, 20000.0, 30000.0, 50000.0, 70000.0, 100000.0, 150000.0])
multi_y = np.array([
[4.22, 4.167, 4.132, 4.038, 4.019, 3.956, 3.884, 3.784, 3.713,
3.633, 3.54, 3.433, 3.358, 3.258, 3.193, 3.128, 3.059, 2.984,
2.934, 2.876, 2.838, 2.798, 2.759],
[0.136, 0.167, 0.188, 0.212, 0.236, 0.257, 0.276, 0.297, 0.309,
0.311, 0.314, 0.311, 0.305, 0.289, 0.277, 0.255, 0.24, 0.218,
0.202, 0.182, 0.168, 0.153, 0.139],
])
n = len(multi_x)
multi_we = np.zeros((2, 2, n), dtype=float)
multi_ifixx = np.ones(n, dtype=int)
multi_delta = np.zeros(n, dtype=float)

multi_we[0,0,:] = 559.6
multi_we[1,0,:] = multi_we[0,1,:] = -1634.0
multi_we[1,1,:] = 8397.0

for i in range(n):
if multi_x[i] < 100.0:
multi_ifixx[i] = 0
elif multi_x[i] <= 150.0:
pass # defaults are fine
elif multi_x[i] <= 1000.0:
multi_delta[i] = 25.0
elif multi_x[i] <= 10000.0:
multi_delta[i] = 560.0
elif multi_x[i] <= 100000.0:
multi_delta[i] = 9500.0
else:
multi_delta[i] = 144000.0
if multi_x[i] == 100.0 or multi_x[i] == 150.0:
multi_we[:,:,i] = 0.0

multi_dat = Data(multi_x, multi_y, wd=1e-4/np.power(multi_x, 2),
we=multi_we)
multi_odr = ODR(multi_dat, multi_mod, beta0=[4.,2.,7.,.4,.5],
delta0=multi_delta, ifixx=multi_ifixx)
multi_odr.set_job(deriv=1, del_init=1)

out = multi_odr.run()
print out.beta
print out.stopreason

My MKL build returns:

[ 4. 2. 7. 0.4 0.5]
['Problem is not full rank at solution', 'Parameter convergence']

However, my openblas version, which passes all the tests, returns:

[ 4.37998803 2.43330576 8.00288459 0.51011472 0.51739023]
['Sum of squares convergence']

Which is what the assertions in the tests are looking for. I haven't looked at the biconjugate gradient failure yet, figured the ODR failures should be tackled first!