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numpydelete-创新互联

手动安装

 sudo rm -rf /usr/local/lib/python2.7/site-packages/numpy/ sudo rm -rf /usr/local/lib/python2.7/site-packages/numpy-*.egg*sudo rm -rf /usr/local/bin/f2py

pip安装
 sudo rm -rf /usr/local/lib/python2.7/dist-packages/numpy/ sudo rm -rf /usr/local/lib/python2.7/dist-packages/numpy-*.egg*sudo rm -rf /usr/local/bin/f2py


export BLAS=~/.local/lib/libopenblas.a
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.local/lib/


30down voteaccepted

I just compiled numpy inside a virtualenv with OpenBLAS integration, and it seems to be working ok. This was my process:

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  1. Compile OpenBlas:

    git clone git://github.com/xianyi/OpenBLAS cd OpenBLAS && make FC=gfortran sudo make PREFIX=/opt/OpenBLAS install sudo ldconfig

  2. Grab the numpy source code:

    git clone https://github.com/numpy/numpy cd numpy

  3. Copy site.cfg.example to site.cfg and edit the copy:

    cp site.cfg.example site.cfg nano site.cfg

    Uncomment these lines:

    .... [openblas] libraries = openblas library_dirs = /opt/OpenBLAS/lib include_dirs = /opt/OpenBLAS/include ....

  4. Check configuration, build, install (optionally in a virutalenv)

    python setup.py config

    The output should look something like this:

    ... openblas_info: FOUND: libraries = ['openblas', 'openblas'] library_dirs = ['/opt/OpenBLAS/lib'] language = f77 FOUND: libraries = ['openblas', 'openblas'] library_dirs = ['/opt/OpenBLAS/lib'] language = f77 ...

    Then just build and install:

    python setup.py build && python setup.py install

  5. Optional: you can use this script to test performance for different thread counts.

    OMP_NUM_THREADS=1 python build/test_numpy.py FAST BLAS version: 1.8.0.dev-27690e3 maxint: 9223372036854775807 dot: 0.100896406174 sec OMP_NUM_THREADS=8 python test_numpy.py FAST BLAS version: 1.8.0.dev-27690e3 maxint: 9223372036854775807 dot: 0.0660264015198 sec

There seems to be a noticeable improvement in performance for higher thread counts. However, I haven't tested this very systematically, and it's likely that for smaller matrices the additional overhead would outweigh the performance benefit from a higher thread count.

share|improve this answer edited Jul 3 at 15:16    answered Jan 18 '13 at 2:50 ali_m
7,0352055
1
 I apply what you did bu tending with foollowing error at your test script /linalg/lapack_lite.so: undefined symbol: zgelsd_ –  Erogol Jan 30 at 17:47
1
 @Erogol Could you check that lapack_lite.so is correctly linked against the libopenblas.so you just built? You can call ldd //numpy/linalg/lapack_lite.so - if you installed OpenBLAS with PREFIX=/usr/local you should see something like libopenblas.so.0 => /usr/local/lib/libopenblas.so.0 in the output. –  ali_m Jan 30 at 18:01
1
 I have following line even I do strictly what you typed above answer. libopenblas.so.0 => /usr/lib/libopenblas.so.0 (0x00007f77e08fc000) –  Erogol Jan 30 at 18:06
 It sounds like numpy has not been built correctly. I would suggest you uninstall the broken copy of numpy, do a python setup.py clean and python setup.py build and look for any error messages during the compilation. –  ali_m Jan 30 at 18:14
 Also, you should probably call sudo ldconfig after installing OpenBLAS if you haven't already (I've added this line to my answer) –  ali_m Jan 30 at 18:21

OMP_NUM_THREADS=7 python test.py

#!/usr/bin/env python
import numpy
import sys
import timeit

try:
import numpy.core._dotblas
print 'FAST BLAS'
except ImportError:
print 'slow blas'

print "version:", numpy.__version__
print "maxint:", sys.maxint
print

x = numpy.random.random((1000,1000))

setup = "import numpy; x = numpy.random.random((1000,1000))"
count = 5

t = timeit.Timer("numpy.dot(x, x.T)", setup=setup)
print "dot:", t.timeit(count)/count, "sec"


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