numpy delete

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:

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 /<path-to-site-packages>/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"