openblas

When I try to import numpy, I get the following error: /usr/local/lib/python2.7/dist-packages/numpy/linalg/__init__.py in <module>() 49 from .info import __doc__ 50 ---> 51 from .linalg import * 52 53 from numpy.testing import Tester /usr/local/lib/python2.7/dist-packages/numpy/linalg/linalg.py in <module>() 27 ) 28 from numpy.lib import triu, asfarray ---> 29 from numpy.linalg import lapack_lite, _umath_linalg 30 from numpy.matrixlib.defmatrix import matrix_power 31 from numpy.compat import asbytes ImportError: /usr/lib/liblapack.so.3: undefined symbol: gotoblas I have already tried solutions

When I set a ulimit -v on a powerful machine I'm working on (48 × Intel(R) Xeon(R) CPU E7-4860 v2 @ 2.60GHz, 2 TB RAM, Red Hat Enterprise Linux Server release 6.7), python fails to import numpy when I'm working in my virtualenv, as shown below. Everything seems fine when I impose no resource limitations or when I'm not working inside virtualenv. I suppose this is a bug somewhere. Did I set up something wrongly? Where is the correct place to report this? NumPy? OpenBLAS? System administrator? $ ulimit -a core file size (blocks, -c) 0 data seg size (kbytes, -d) unlimited scheduling priority (-e)

The BLAS section in R installation and administration manual says that when R is built from source , with configuration parameter --without-blas , it will build Netlib's reference BLAS into a standalone shared library at R_HOME/lib/libRblas.so , along side the standard R shared library R_HOME/lib/libR.so . This makes it easier for user to switch and benchmark different tuned BLAS in R environment. The guide suggests that researcher might use symbolic link to libRblas.so to achieve this, and this article gives more details on this. On contrary, when simply installing a pre-compiled binary

I am currently trying to follow a simple example for parallelizing a loop with cython's prange. I have installed OpenBlas 0.2.14 with openmp allowed and compiled numpy 1.10.1 and scipy 0.16 from source against openblas. To test the performance of the libraries I am following this example: http://nealhughes.net/parallelcomp2/ . The functions to be timed are copied form the site: import numpy as np from math import exp from libc.math cimport exp as c_exp from cython.parallel import prange,parallel def array_f(X): Y = np.zeros(X.shape) index = X > 0.5 Y[index] = np.exp(X[index]) return Y def c

I am learning deep learning recently and my friend recommended me caffe. After install it with OpenBLAS, I followed the tutorial, MNIST task in the doc. But later I found it was super slow and only one CPU core was working. The problem is that the servers in my lab don't have GPU, so I have to use CPUs instead. I Googled this and got some page like this . I tried to export OPENBLAS_NUM_THREADS=8 and export OMP_NUM_THREADS=8 . But caffe still used one core. How can I make caffe use multi CPUs? Many thanks. @Karthik. That also works for me. One interesting discovery that I made was that using 4

For example can I have Atlas , OpenBlas , MKL installed on my Ubuntu 14.04 at the same time and switch between them without recompiling Caffe ? Sure, you have to have them installed, and on Ubuntu/Debian issue command update-alternatives --config libblas.so.3 You'll get numbered list of alternatives and could switch between them easily Link: https://wiki.debian.org/DebianScience/LinearAlgebraLibraries Yes, you can. You just need to use dynamic library linking. The libblas.so.3 is a soft linking to /etc/alternatives/libblas.so.3 Which in turn points to the BLAS library implementation For