intel-mkl

Has anyone succeeded to build tensorflow python wheel with the following configuration: CPU (not GPU) OS: Windows 7 / server 2012 Using Intel MKL and/or mkl-dnn Python 3.6 I am struggling, for days now, trying to tweak bazel files and cmake files without success. Would like to know if someone succeeded and willing to share what he did . Thanks, Lior Solution: 1 Installing TensorFlow from Source on windows is not generally supported. But you can try building this using bazel or tensorflow cmake build. Refer: https://www.tensorflow.org/install/install_sources Solution: 2 Create Intel optimized

I have built a custom package with some functions written in RcppEigen. I also have Microsoft R open with Intel MKL enabled. How could I link the R package to the Intel MKL feature? Setup 1 : Below are procedures that I have tried to link the package with MKL in the normal R, but failed: The Eigen documents says I need: 1. #define EIGEN_USE_MKL_ALL 2. link your program to MKL libraries ( the MKL linking advisor ) Based on 2, in my file Makevars PKG_CXXFLAGS = -I/opt/intel/mkl/include PKG_LIBS = ${LAPACK_LIBS} ${BLAS_LIBS} ${FLIBS} -L/opt/intel/mkl/lib/intel64 -Wl,--no-as-needed -lmkl_intel

Fast Fourier Transforms (FFTs) in Numpy/SciPy are not threaded. Enthought Python is shipped with the Intel MKL numerical library, which is capable of threaded FFTs. How does one get access to these routines? The following code works for me with Enthought 7.3-1 (64-bit) on Windows 7 Ultimate 64-bit. I haven't benchmarked it but it certainly uses all cores at once rather than just one. from ctypes import * class Mkl_Fft: c_double_p = POINTER(c_double) def __init__(self,num_threads=8): self.dfti = cdll.LoadLibrary("mk2_rt.dll") self.dfti.MKL_Set_Num_Threads(num_threads) self.Create = self.dfti

I am using Enthought's Canopy/EPD version of python which ships with numpy linked against MKL. I am currently running a set of simulations in parallel (using PP) on both my work computer (Windows 7, Quad Core i5 @ 3.33 Ghz, 4 GB ram) and my home workstation (Mac Pro 3.1, Ubuntu 12.04, 2x Quad Core Xeon @ 2.8 Ghz, 6 GB ram). But when I benchmark my simulations, they run much quicker on the work computer (35 seconds per iteration vs. 60 on the mac pro). The problem is being perfectly balanced between cores (embarrassingly parallel problem), so I suspect there is an issue with the MKL library on

I am running a python script and I get this error: Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so. Both files are present in the anaconda2/lib directory. How can I fix this error? Thanks. Zaikun Xu If you use conda, try with these two commands: conda install nomkl numpy scipy scikit-learn numexpr conda remove mkl mkl-service It should fix your problem. I ran into this problem after installing anaconda3 (vesion 4.2.0). The fix for me was simple, and I was able to keep using mkl. Just update to the latest numpy version. conda update numpy Just wanted to note that Anaconda 4.0