Under linux or windows?

Mac OS X Lion (10.7), can anybody reproduce?

And cross_val_score with njobs=2 works in the same IPython session? This is really weird. Could you try to add print statements or pdb.set_trace() (the latter might be complicated in a multiprocessing env with piped stdin and stdout...).

Not in the same session because the process needs to be killed, but it works in an identical IPython session in the same virtualenv. I don't really know how to debug this, I'll try some prints to see where it hangs.

Vlad

On Feb 16, 2012, at 12:15 , Olivier Grisel wrote:

And cross_val_score with njobs=2 works in the same IPython session? This is really weird. Could you try to add print statements or pdb.set_trace() (the latter might be complicated in a multiprocessing env with piped stdin and stdout...).

---

Reply to this email directly or view it on GitHub:
#636 (comment)

After a heavy tracing session I (strangely) found the hanging line to be a call to np.dot (!) in safe_sparse_dot called from euclidean_distances in k_init.

The exact same code runs when n_jobs=1 (ie. avoiding the special branch that bypasses the call to Parallel when n_jobs=1)

The interesting part (though I haven't checked this extensively) is that this happens in all 3 of my virtualenvs: stable numpy&scipy, git head numpy&scipy and scipy-superpack.

Any ideas?

On Feb 16, 2012, at 13:15 , Olivier Grisel wrote:

And cross_val_score with njobs=2 works in the same IPython session? This is really weird. Could you try to add print statements or pdb.set_trace() (the latter might be complicated in a multiprocessing env with piped stdin and stdout...).

---

Reply to this email directly or view it on GitHub:
#636 (comment)

Can you try to attach a gdb to the running python processes and get a backtrace?

$ sudo gdb python <pid of one of the hanging python processes>
(gdb) bt  # get the backtrace for the C program (python + libatlas.so)
(gdb) py-bt # get the python interpreter stacktrace, just to check

I'm trying to run scikit-learn for the first time on my Mac and am getting a hang on sklearn.cluster.tests.test_k_means.test_k_means_plus_plus_init_2_jobs also.

Mac OS X Lion (10.7), Python 2.7, latest scikit-learn master from Github, virtualenv, numpy==1.6.1, scipy==0.10.1

I haven't tried gdb or anything yet. I'll be at the PyData Workshop tomorrow (Saturday) in case someone is there and wants to poke around on my computer.

I did a Git bisect to narrow it down:

1520ad3ebb337ae44d1f4398ce15826c871a098d is the first bad commit
commit 1520ad3ebb337ae44d1f4398ce15826c871a098d
Author: Robert Layton <robertlayton@gmail.com>
Date:   Sun Jan 29 13:13:03 2012 +1100

    Parallel version of k-means.

:040000 040000 5d86a2a99b9ef029b32a7bcc8810c7ddb318521d 238d5519d937c1fa340dba051f9e41f4ede3421b M  doc
:040000 040000 d9b083cee7addc2c403cb2d95458c411c5482a48 260f9a8fcbd3ac7b6d03fb4785193015d7d9bb0c M  sklearn

So far I have been unable to get GDB working with Python. This probably isn't useful, but here's what I get when I Ctrl-C it after it hangs:

^CProcess PoolWorker-15:
Process PoolWorker-16:
Traceback (most recent call last):
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/pool.py", line 85, in worker
    task = get()
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/queues.py", line 376, in get
    racquire()
KeyboardInterrupt
Traceback (most recent call last):
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/pool.py", line 85, in worker
    task = get()
  File "/usr/local/Cellar/python/2.7.2/lib/python2.7/multiprocessing/queues.py", line 378, in get
    return recv()
KeyboardInterrupt

Ok so the git bisect tells us that this bug was introduced at the same time as we introduce the parallel kmeans feature itself. The python stacktrace might tell use that there is a race condition in multiprocessing:

this might be related: to http://bugs.python.org/issue9205 which has been fixed in python 3 but maybe not backported in python 2.7.

By the way @vene and @njwilson, which versions of python are you using?

Edit: I mean exactly, such as python --version

I'm using Python 2.7.2

Same here... I could never reproduce this race condition. So this might not be linked to an already backported python bugfix.

How many cores / CPUs do you have on your machine? I am running a dual core machine.

Same. Dual core.

@njwilson can you try to checkout and build this branch and see if you can still reproduce the issue?

https://github.com/ogrisel/scikit-learn/compare/no-kmeans-profile

cython profiling seems to be messing around with python threads and I have heard recently that python forking and the the python threads are not good friends: http://bugs.python.org/issue6721

I tried the no-kmeans-profile branch and the problem still exists.

@cournape and I tracked the bug down to a segfault in Grand Central Dispatch on the BLAS DGEMM function (only after a fork):

https://gist.github.com/2027412

I have just reported the issue to Apple. Hopefully it will be fixed at some point. In the mean time we will have to skip this test on OSX I think.

Wow, great work!

Let's hope they are responsive.

On Mar 13, 2012, at 09:51 , Olivier Grisel wrote:

@cournape and I tracked the bug down to a segfault in Grand Central Dispatch on the BLAS DGEMM function (only after a fork):

https://gist.github.com/2027412

I have just reported the issue to Apple. Hopefully it will be fixed at some point. In the mean time we will have to skip this test on OSX I think.

---

Reply to this email directly or view it on GitHub:
#636 (comment)

Now (on Mountain Lion) I don't get hanging but I get random segfaults in the sklearn.cluster.tests.test_k_means.test_k_means_plus_plus_init_2_jobs.
This is the output: http://pastebin.com/aDnYUBkX

I assume this is the same bug @ogrisel and @cournape found, that Apple confirmed as expected behaviour (you can't call accelerate in subprocesses)

Can I compile my own parallel, optimized blas and lapack then? Did anybody do this on Lion or Mountain Lion because I can't find much about this endeavour.

Accelerate can be called after a fork but you need to execv the subprocess with the python binary (which multiprocessing does not do under posix). I think @cournape is working on a numpy build that uses OpenBlas and that could server as a good alternative for Accelerate (even though early benchmark should a slight decrease in performances).

So is there any way we can avoid these problems on OS X? Can we check the blas before trying to do something in parallel?

I think @cournape would like to make it possible to switch the blas / lapack implementation for numpy at runtime but that might be pretty tricky to implement and will obviously only be available in some undetermined future version of numpy if ever.

I don't know if we can detect whether the Blas in used is linked to Accelerate and issue a warning in joblib prior to forking in that case.

Hm... ok probably there is no chance really... should we just add this to the docs as a known issue in OS X?

+1 it never hurts to document stuff.

does anybody know if EPD on osx produces the same bug?

does anybody know if EPD on osx produces the same bug?

I use EPD with MKL on my mac and I've never had a problem so I would
say no until I'm proved wrong...

I added a warning in bd3a6c2.
@ogrisel @vene could you please check that it is accurate? Should we close this issue or leave it open?

Hanging on a clean install with Anaconda on a new i7 Mabook Pro Retina and
Mavericks.

Which version of scikit-learn? We believe that we have fixed this in
master.

Hey @GaelVaroquaux, I'm using scikit-learn 0.14.1 np17py27_1 from Anaconda.

@kevindavenport can you please give us the output of:

from numpy.distutils.system_info import get_info
print(get_info('blas_opt'))

If you have gdb installed, can you also try?

$ sudo gdb python <pid of one of the hanging python processes>
(gdb) bt  # get the backtrace for the C program (python + libatlas.so)
(gdb) py-bt # get the python interpreter stacktrace, just to check

@ogrisel Thanks for your help. The dedication you guys have to this project is incredible!

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

I found an article stating how I can install gdb with homebrew:

brew install https://raw.github.com/Homebrew/homebrew-dupes/master/gdb.rb

but I also read "gdb has been replaced by lldb, and is no longer supported. gcc and llvm-gcc are also gone, replaced by clang."

Alright: anaconda is apparently linked against OSX vecLib instead of MKL or Atlas which causes the issue. Therefore this is the same issue as previously. We don't have any clean solution for this in the short term besides advising you to build Atlas from source to use that instead of vecLib.

Peter Wang from continuum states that they build against Apple Accelerate Framework. Do any of you guys have experience with pointing my environment to Atlas instead?

Maybe numpy.distutils.system_info is lying then? Anyway the freeze looks real and AFAIK MKL is fork-safe.

Numpy developers started working on a binary package for numpy on OSX that builds against an embedded Atlas that does not suffer from this bug, this the comments in numpy/numpy#4007. If this experiment is validated a such numpy whl packages get published on PyPI we can close this bug and re-enable the skipped tests for future versions of numpy.

A few months ago I decided to just conda install MKL https://store.continuum.io/cshop/mkl-optimizations/ which would make my python install use the Intel MKL optimizations over the built in accelerate framework. This let me use the parallel features of joblib in scikit, but recently the anaconda 2.0 and/or numpy upgrade broke it again. Everything checks out here though:

from numpy.distutils.system_info import get_info
print(get_info('blas_opt'))
print(get_info('lapack_opt'))

{'libraries': ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread'], 'library_dirs': ['/Users/balthasar/anaconda/lib'], 'define_macros': [('SCIPY_MKL_H', None)], 'include_dirs': ['/Users/balthasar/anaconda/include']}
{'libraries': ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'iomp5', 'pthread'], 'library_dirs': ['/Users/balthasar/anaconda/lib'], 'define_macros': [('SCIPY_MKL_H', None)], 'include_dirs': ['/Users/balthasar/anaconda/include']}

This is bad news. Can you confirm that the following breaks with numpy + mkl?

import numpy as np
from joblib import Parallel, delayed

a = np.random.randn(3000, 3000)

# Force initialization of multithreaded BLAS context in main process
x = np.dot(a, a.T)

if __name__ == "__main__":
    # Force usage of of multithreaded BLAS in fork-child processes
    Parallel(n_jobs=2)(delayed(np.dot)(a, a.T) for _ in range(3))
    print('ok')

If you get a segfault with the OSX process crash report, please report the stack trace here.

I close this issue as the original bug has now been fixed by the new numpy wheel package on PyPI.

@kevindavenport could you please open a new issue for if you can reproduce the crash with numpy MKL and the code snippet provided in my earlier comment?

I am still getting this bug with the latest versions of everything on a mac. numpy + mkl breaks.

mkl, not accelerate? I didn't realize that was a problem, too.
You are not using the anaconda version, are you?

I'm getting this with Python 2.7.10, Anaconda 2.3, OS X 10.10.5, scikit-learn 16.1

Not sure if this will help:

Process: python [1515]
Path: /anaconda/python.app/Contents/MacOS/python
Identifier: python
Version: ???
Code Type: X86-64 (Native)
Parent Process: python [1483]
Responsible: Terminal [603]

Date/Time: 2015-08-24 13:52:01.586 -0500
OS Version: Mac OS X 10.10.5 (14F27)
Report Version: 11

Time Awake Since Boot: 1500 seconds

Crashed Thread: 0 Dispatch queue: com.apple.main-thread

Exception Type: EXC_BAD_ACCESS (SIGSEGV)
Exception Codes: KERN_INVALID_ADDRESS at 0x0000000000000110

VM Regions Near 0x110:
-->
__TEXT 0000000100000000-0000000100001000 [ 4K] r-x/rwx SM=COW /anaconda/python.app/Contents/MacOS/python

Application Specific Information:
*** multi-threaded process forked ***
crashed on child side of fork pre-exec

Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 libdispatch.dylib 0x00007fff94b23c13 dispatch_group_async + 533
1 libBLAS.dylib 0x00007fff872bb1ff rowMajorNoTranspose + 689
2 libBLAS.dylib 0x00007fff872b92c9 cblas_dgemv + 783
3 _dotblas.so 0x00000001038c49ab gemv + 123
4 _dotblas.so 0x00000001038c38c8 dotblas_matrixproduct + 4312
5 libpython2.7.dylib 0x00000001000c39bd PyEval_EvalFrameEx + 24989
6 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
7 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
8 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
9 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
10 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
11 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
12 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
13 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
14 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
15 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
16 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
17 libpython2.7.dylib 0x00000001000bea0a PyEval_EvalFrameEx + 4586
18 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
19 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
20 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
21 libpython2.7.dylib 0x000000010001dccd instancemethod_call + 333
22 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
23 libpython2.7.dylib 0x000000010007966a slot_tp_call + 74
24 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
25 libpython2.7.dylib 0x00000001000bea0a PyEval_EvalFrameEx + 4586
26 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
27 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
28 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
29 libpython2.7.dylib 0x00000001000bea0a PyEval_EvalFrameEx + 4586
30 libpython2.7.dylib 0x00000001000c505b PyEval_EvalFrameEx + 30779
31 libpython2.7.dylib 0x00000001000c505b PyEval_EvalFrameEx + 30779
32 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
33 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
34 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
35 libpython2.7.dylib 0x000000010001dccd instancemethod_call + 333
36 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
37 libpython2.7.dylib 0x0000000100079298 slot_tp_init + 88
38 libpython2.7.dylib 0x0000000100076115 type_call + 245
39 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
40 libpython2.7.dylib 0x00000001000c071d PyEval_EvalFrameEx + 12029
41 libpython2.7.dylib 0x00000001000c505b PyEval_EvalFrameEx + 30779
42 libpython2.7.dylib 0x00000001000c505b PyEval_EvalFrameEx + 30779
43 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
44 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
45 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
46 libpython2.7.dylib 0x00000001000bea0a PyEval_EvalFrameEx + 4586
47 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
48 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
49 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
50 libpython2.7.dylib 0x00000001000bea0a PyEval_EvalFrameEx + 4586
51 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
52 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
53 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
54 libpython2.7.dylib 0x000000010001dccd instancemethod_call + 333
55 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
56 libpython2.7.dylib 0x0000000100079298 slot_tp_init + 88
57 libpython2.7.dylib 0x0000000100076115 type_call + 245
58 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
59 libpython2.7.dylib 0x00000001000bea0a PyEval_EvalFrameEx + 4586
60 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
61 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
62 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
63 libpython2.7.dylib 0x000000010001dccd instancemethod_call + 333
64 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
65 libpython2.7.dylib 0x000000010007966a slot_tp_call + 74
66 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
67 libpython2.7.dylib 0x00000001000c071d PyEval_EvalFrameEx + 12029
68 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
69 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
70 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
71 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
72 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
73 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
74 libpython2.7.dylib 0x00000001000c505b PyEval_EvalFrameEx + 30779
75 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
76 libpython2.7.dylib 0x00000001000c5275 PyEval_EvalFrameEx + 31317
77 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
78 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
79 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
80 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
81 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
82 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
83 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
84 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
85 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
86 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
87 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
88 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
89 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
90 libpython2.7.dylib 0x000000010003e1d0 function_call + 176
91 libpython2.7.dylib 0x000000010000c7b2 PyObject_Call + 98
92 libpython2.7.dylib 0x00000001000bea0a PyEval_EvalFrameEx + 4586
93 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
94 libpython2.7.dylib 0x00000001000c3d10 PyEval_EvalFrameEx + 25840
95 libpython2.7.dylib 0x00000001000c5ca3 PyEval_EvalCodeEx + 2115
96 libpython2.7.dylib 0x00000001000c5dc6 PyEval_EvalCode + 54
97 libpython2.7.dylib 0x00000001000eb17e PyRun_FileExFlags + 174
98 libpython2.7.dylib 0x00000001000eb41a PyRun_SimpleFileExFlags + 458
99 libpython2.7.dylib 0x000000010010248d Py_Main + 3165
100 com.continuum.python 0x0000000100000f54 start + 52

Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000109a93000 rbx: 0x0000000000000000 rcx: 0x0000000000000000 rdx: 0x0000000000000100
rdi: 0x00007fff78b15d40 rsi: 0x0000000000000000 rbp: 0x00007fff5fbf8090 rsp: 0x00007fff5fbf8060
r8: 0x0000000109a93000 r9: 0x0000000000000000 r10: 0x0000000000000004 r11: 0xffff8001a07eaaa0
r12: 0x00000001003e2c00 r13: 0x00000001003cf200 r14: 0x00007fff94b1d317 r15: 0x0000000000002000
rip: 0x00007fff94b23c13 rfl: 0x0000000000010206 cr2: 0x0000000000000110

Logical CPU: 6
Error Code: 0x00000006
Trap Number: 14

Binary Images:
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0x100003000 - 0x100168ff3 +libpython2.7.dylib (2.7) <9FC353D3-C359-264F-E3D8-67C5F0FCE7FD> /anaconda//libpython2.7.dylib
0x1002ed000 - 0x1002efff7 +locale.so (???) /anaconda//locale.so
0x1002f3000 - 0x1002f7fff +struct.so (???) <1F5B3CB4-57FD-8018-05E8-DBBCC283E1D1> /anaconda//struct.so
0x1005f0000 - 0x1005f6fff +json.so (???) /anaconda//json.so
0x1005fb000 - 0x1005fcff7 +heapq.so (???) <081C8296-142B-7E34-1939-275C1861BFB9> /anaconda//heapq.so
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0x10228f000 - 0x102291ff7 +binascii.so (???) /anaconda//binascii.so
0x102295000 - 0x1022adffe +libz.1.2.8.dylib (1.2.8) <5660EDF3-A9D4-49B6-FA19-B09B10037896> /anaconda//libz.1.2.8.dylib
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0x1024fd000 - 0x1024feff7 +random.so (???) /anaconda//random.so
0x102501000 - 0x102516fff +io.so (???) <16E7468F-CB05-F642-D297-78A663A9BFAD> /anaconda//io.so
0x10252d000 - 0x10252eff7 +fcntl.so (???) <1EF544FB-D4E8-5F53-5608-BB570BDD2CC5> /anaconda//fcntl.so
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0x10257e000 - 0x10257fff7 +termios.so (???) /anaconda//termios.so
0x102744000 - 0x10274dff7 +socket.so (???) /anaconda//socket.so
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0x1027b4000 - 0x1027c2fff +datetime.so (???) /anaconda//datetime.so
0x1027ce000 - 0x1027d9fff +sqlite3.so (???) /anaconda//sqlite3.so
0x1027e5000 - 0x1027f5fff +cPickle.so (???) /anaconda//cPickle.so
0x1027fd000 - 0x1027fdfff +check_build.so (???) <269F80A3-A7E6-3B3B-AED0-8FAE11E2EB48> /anaconda//check_build.so
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0x1038c1000 - 0x1038c5ff7 +dotblas.so (???) /anaconda//dotblas.so
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0x103c1a000 - 0x103c70fff +umath.so (???) <65DFA61C-D764-3CA5-BA1C-1937CCBA6C0E> /anaconda//umath.so
0x103d5c000 - 0x103d60ff7 +compiled_base.so (???) /anaconda//compiled_base.so
0x103da4000 - 0x103db3fff +umath_linalg.so (???) /anaconda//umath_linalg.so
0x105000000 - 0x105000fff +future_builtins.so (???) /anaconda//future_builtins.so
0x105003000 - 0x10500bfff +fftpack_lite.so (???) /anaconda//fftpack_lite.so
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0x1056b1000 - 0x1056bcfff +murmurhash.so (???) /anaconda//murmurhash.so
0x1056c5000 - 0x1057f9fff +ufuncs.so (???) /anaconda//ufuncs.so
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0x10588d000 - 0x10595afff +specfun.so (???) /anaconda//specfun.so
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0x105bf8000 - 0x105c69ff7 +cython_lapack.so (???) <03DDF96F-9D18-3081-909A-4E28B6FD554C> /anaconda//cython_lapack.so
0x105cfc000 - 0x105d07ff7 +ellip_harm_2.so (???) /anaconda//ellip_harm_2.so
0x105d10000 - 0x105d4cff7 +odepack.so (???) /anaconda//odepack.so
0x105d57000 - 0x105d8bfff +quadpack.so (???) <8C8CDD06-D4DC-36DE-9962-DABE9F7AAF0C> /anaconda//quadpack.so
0x105d96000 - 0x105dd9fe7 +vode.so (???) <0D206356-2716-3282-8D02-B3A3435E5449> /anaconda//vode.so
0x105de5000 - 0x105e12fff +dop.so (???) /anaconda//dop.so
0x105e1d000 - 0x105e5bfe7 +lsoda.so (???) <1F373887-43C5-3E70-AE98-AD9E1F1ABDC1> /anaconda//lsoda.so
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0x105ef4000 - 0x105ef9fff +array.so (???) /anaconda//array.so
0x106b00000 - 0x106b47ff7 +superlu.so (???) <632D1937-5E8B-3976-A9BA-AA35A62EE887> /anaconda//superlu.so
0x106b5f000 - 0x106bfdfef +arpack.so (???) /anaconda//arpack.so
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0x106cc0000 - 0x106d0eff4 +libjpeg.8.dylib (13) /anaconda//libjpeg.8.dylib
0x106d15000 - 0x106d8afff +libtiff.5.dylib (7) <75EC4D30-54D3-8446-CB2C-3CCAFA5233A3> /anaconda//libtiff.5.dylib
0x106e18000 - 0x106e1bff7 +mmap.so (???) /anaconda//mmap.so
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0x106e6b000 - 0x106e7aff7 +sparsefuncs_fast.so (???) <58288B83-4354-35B0-BCCE-A7275A79BF8F> /anaconda//sparsefuncs_fast.so
0x106e84000 - 0x106e88fff +futil.so (???) /anaconda//futil.so
0x106ecc000 - 0x106eeefff +minpack2.so (???) <29B6D198-9292-3B02-8E40-70FC27F1C733> /anaconda//minpack2.so
0x106ef7000 - 0x106f2cfff +lbfgsb.so (???) <20A2B373-2D36-3AE5-8D52-1E656C8B959F> /anaconda//lbfgsb.so
0x106f36000 - 0x106f3cff7 +moduleTNC.so (???) <1EA62A79-B217-3D5D-AEEC-93EFD6843084> /anaconda//moduleTNC.so
0x106f3f000 - 0x106f70ff7 +cobyla.so (???) <96BE24C4-D27B-3834-B957-CF97CCB53AB5> /anaconda//cobyla.so
0x106f79000 - 0x106fa6fef +slsqp.so (???) <0674BBA5-B905-3128-994B-9B263ADEEEF8> /anaconda//slsqp.so
0x106ff0000 - 0x106ff1ff7 +zeros.so (???) <7A6F9EA2-82CD-3CBB-BAC1-A7A9F50083AB> /anaconda//zeros.so
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0x1078c8000 - 0x1078d3ff7 +vonmises_cython.so (???) <1521A621-918E-3F28-8628-AB9F2876766A> /anaconda//vonmises_cython.so
0x1079db000 - 0x1079ebfff +rank.so (???) /anaconda//rank.so
0x1079f6000 - 0x1079f7ff7 +lgamma.so (???) <0200625F-3A19-3494-AD06-5F214B8240A4> /anaconda//lgamma.so
0x1079fb000 - 0x1079fcff7 +typedefs.so (???) /anaconda//typedefs.so
0x107b00000 - 0x107b07fff +statlib.so (???) /anaconda//statlib.so
0x107b0d000 - 0x107b36fff +mvn.so (???) <52BCD238-AB6A-3137-829E-8272B2D3FD2B> /anaconda//mvn.so
0x107c77000 - 0x107ca6fff +ckdtree.so (???) /anaconda//ckdtree.so
0x107cc0000 - 0x107d5dff7 +qhull.so (???) /anaconda//qhull.so
0x107dd1000 - 0x107de0ff7 +expected_mutual_info_fast.so (???) <71F05DEB-E92E-377B-AFD6-5CB97D0D1A27> /anaconda//expected_mutual_info_fast.so
0x107de9000 - 0x107e0bff7 +pairwise_fast.so (???) <66BD6A36-AD00-34F8-B3DE-3B1B62F10E86> /anaconda//pairwise_fast.so
0x107e61000 - 0x107ea4fff +ball_tree.so (???) /anaconda//ball_tree.so
0x107ec9000 - 0x107ed3ff7 +random.so (???) <77A0847C-BC01-3B51-8D19-36AF20569F30> /anaconda//random.so
0x107edc000 - 0x107ee8fff +graph_shortest_path.so (???) /anaconda//graph_shortest_path.so
0x107ef0000 - 0x107ef3fff +arrayfuncs.so (???) <5559E3EB-2244-3BB9-B720-90993BAFE659> /anaconda//arrayfuncs.so
0x107ef9000 - 0x107ef9ff7 +bisect.so (???) /anaconda//bisect.so
0x108000000 - 0x108026fff +dist_metrics.so (???) <46A70311-D971-3C2F-9715-E7922C21FB79> /anaconda//dist_metrics.so
0x108047000 - 0x10808aff7 +kd_tree.so (???) /anaconda//kd_tree.so
0x1080ee000 - 0x1080f5ff7 +weight_vector.so (???) <172FF6C4-8194-3962-9AE7-E54356CD1016> /anaconda//weight_vector.so
0x108200000 - 0x108213fff +traversal.so (???) <9B71C875-8BB1-3E6F-BF08-5A7F29D4DDB3> /anaconda//traversal.so
0x108220000 - 0x108232ff7 +graph_tools.so (???) /anaconda//graph_tools.so
0x108280000 - 0x1082cafef +fitpack.so (???) <090A1B32-D7BC-32C3-B204-7BC0FC7ABE72> /anaconda//fitpack.so
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0x108358000 - 0x108387ff7 +ppoly.so (???) /anaconda//ppoly.so
0x1083a0000 - 0x1083d7ff7 +interpnd.so (???) <4F62D658-B581-3751-A502-D3CD6860CBA5> /anaconda//interpnd.so
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0x1084ad000 - 0x1084c2fff +sgd_fast.so (???) /anaconda//sgd_fast.so
0x1084d4000 - 0x1084ddff7 +seq_dataset.so (???) /anaconda//seq_dataset.so
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0x10856d000 - 0x108580ff7 +liblinear.so (???) <755AB5B0-6DD9-3942-BF5A-638C658C26B7> /anaconda//liblinear.so
0x10858c000 - 0x1085baff7 +libsvm_sparse.so (???) /anaconda//libsvm_sparse.so
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0x1086b8000 - 0x1086d1fff +fast_dict.so (???) /anaconda//fast_dict.so
0x1086e6000 - 0x1086edfff +dbscan_inner.so (???) <397AAD32-5EE4-34D2-92A1-69149BDF4A99> /anaconda//dbscan_inner.so
0x108734000 - 0x10873cfff +elementtree.so (???) <50156711-A9F5-81D6-88A8-DFEE1B595B3C> /anaconda//elementtree.so
0x108782000 - 0x1087b6feb +pyexpat.so (???) <86713AAD-1394-620D-3377-5EF7941B9F7C> /anaconda//pyexpat.so
0x1087c8000 - 0x10885cfff +unicodedata.so (???) /anaconda//unicodedata.so
0x1089f7000 - 0x1089fafff +csv.so (???) /anaconda//csv.so
0x108b40000 - 0x108b49fff +tkinter.so (???) /anaconda//tkinter.so
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0x109fd4000 - 0x109fdffff +streams.so (???) <26A8DC05-C029-33F1-82B6-784F97CBDDF5> /anaconda//streams.so
0x109fea000 - 0x109ff0fff +svmlight_format.so (???) <6920ED23-4E6F-3E5B-B05B-8349772C7CD8> /anaconda//svmlight_format.so
0x10a100000 - 0x10a11cfff +mio5_utils.so (???) /anaconda//mio5_utils.so
0x10a22e000 - 0x10a284fff +_regex.so (???) <0D3E3CCF-0D62-39D7-A7E5-65FB20DC1C70> /anaconda/*/_regex.so
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0x7fff86914000 - 0x7fff86915fff liblangid.dylib (117) /usr/lib/liblangid.dylib
0x7fff86916000 - 0x7fff86afbff7 libicucore.A.dylib (531.48) <3CD34752-B1F9-31D2-865D-B5B0F0BE3111> /usr/lib/libicucore.A.dylib
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0x7fff87110000 - 0x7fff87119ff7 libsystem_notify.dylib (133.1.1) <61147800-F320-3DAA-850C-BADF33855F29> /usr/lib/system/libsystem_notify.dylib
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0x7fff87573000 - 0x7fff8757cfff libsystem_pthread.dylib (105.40.1) /usr/lib/system/libsystem_pthread.dylib
0x7fff875d4000 - 0x7fff87683fe7 libvMisc.dylib (516) <6739E390-46E7-3BFA-9B69-B278562326E6> /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libvMisc.dylib
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0x7fff87a9b000 - 0x7fff87a9bfff com.apple.Carbon (154 - 157) <9BF51672-1684-3FDE-A561-FC59A2864EF8> /System/Library/Frameworks/Carbon.framework/Versions/A/Carbon
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0x7fff87e28000 - 0x7fff87e6eff7 libauto.dylib (186) /usr/lib/libauto.dylib
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0x7fff87f16000 - 0x7fff87f4efff libsystem_network.dylib (412.20.3) <6105C134-6722-3C0A-A4CE-5E1261E2E1CC> /usr/lib/system/libsystem_network.dylib
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can you give us

from numpy.distutils.system_info import get_info
print(get_info('blas_opt'))

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

@ogrisel can you remind me of the details with accelerate?

@AWNystrom you are using apple accelerate blas, which is known to not cooperate with multi-processing. Did you install numpy via conda?
If you are in an academic setting, you could get the conda accelerate package (yes that is a different accelerate), which contains numpy linked against MKL.
Or you could build a numpy yourself and link it against a different blas.
I'm not sure if there are is a free conda numpy that for OS X that is not linked against accelerate.

@ogrisel can you remind me of the details with accelerate?

The problem is that multiprocessing does a fork without an exec. Many libraries like (some versions of) Accelerate / vecLib, (some versions of) MKL, the OpenMP runtime of GCC, nvidia's cuda (and probably many others), manage their own internal thread pool. Upon a syscall to fork, the thread pool state in the child process is corrupted: the thread pool things it has many threads while only the main thread state has been forked. It's possible to change the libraries to make them detect when a fork happens and reinitialize the thread pool in that case: we did that for OpenBLAS (merged upstream in master since 0.2.9) and we contributed a patch (not yet reviewed) to GCC's OpenMP runtime.

In the end the real culprit is Python's multiprocessing that does fork without exec (to reduce the overhead of starting and using new Python process for parallel computing, it's kind of a hack). This is a violation of the POSIX standard and therefore organizations like Apple refuse to consider the lack of fork-safety in Accelerate / vecLib as a bug.

In Python 3.4+ it's now possible to configure multiprocessing to use the 'forkserver' or 'spawn' start methods (instead of the default 'fork') to manage the process pools. This should make it possible to not be subject to this issue anymore. We don't use it by default in joblib because it causes some overhead and would make the default behavior slightly different in Python 2.7 and Python 3.4+. Maybe we should change the default to 'forkserver' under POSIX to have this problem disappear for Python 3.4+ users.

Thank you for the great explanation. Should we add this / a short version of this to the FAQ? And open a dedicated fork-savety issue to keep track?

Thanks, Andreas. I have some latent memory of having to change numpy versions to get something to work and doing it via pip.

Thank you for the great explanation. Should we add this / a short version of this to the FAQ?

Good idea, I am on it.

In addition to documenting the problem in the scikit-learn FAQ I went ahead and make joblib use the forkserver start method by default under POSIX system with Python 3.4 or later: joblib/joblib#232.

Feedback appreciated.