Intel Math Kernel Library (MKL) for Intel based systems accelerate math processing routines, increase application performance, and reduce development time. This ready-to-use math library includes:
Linear Algebra | Fast Fourier Transforms (FFT) | Deep Neural Networks | Vector Statistics & Data Fitting | Vector Math & Miscellaneous Solvers
This is my first successful build of Tensorflow which has integrated with MKL-DNN 2018 Initial Release. You can download the compiled pip wheel file (.whl) through Release section. This build has enabled support of AVX, SSE4 features on Intel CPU for better performance.
Please remind that this build is NOT for CUDA GPU. The intention to utilize Intel MKL is to accelerate Intel Core i5 (Haswell) or above CPU (not GPU) on Mac computer which has well-known limit of OpenCL support on its integrated Graphic processor (not even has SYCL/ComputeCPP support for Mac at the moment).
For OpenCL 1.2 support release of Tensorflow, please see https://github.com/hughperkins/tf-coriander.
Please remind that this is just a guideline and you might want to extend it a bit further with the latest version of Tensorflow framework. Let us know if you have any success on it.
System: Mac OS X 10.12.6
Intel MKL-DNN library: mklml_mac_2018.0.20170908.tgz (from https://github.com/01org/mkl-dnn/releases)
Tensorflow release: 1.3.1 (https://github.com/tensorflow/tensorflow/releases)
Bazel release: 0.5.4 (https://github.com/bazelbuild/bazel/releases)
Installed MKL to /opt/intel/mklml
$ tar -xvzf mklml_mac_2018.0.20170908.tgz
$ mv mklml_mac_2018.0.20170908 /opt/intel/mklml
Symlink the two .dylib files (lib/libmklml.dylib, lib/libiomp5.dylib) to /usr/local/lib
$ ln -sf /opt/intel/mklml/lib/libmklml.dylib /usr/local/lib/libmklml.dylib
$ ln -sf /opt/intel/mklml/lib/libiomp5.dylib /usr/local/lib/libiomp5.dylib
Remember to assign proper file ownership of the .dylib links in order to avoid errors at the later stage:
$ chown $(whoami):staff /usr/local/lib/libmklml.dylib
$ chown $(whoami):staff /usr/local/lib/libiomp5.dylib
Assign correct 'install name' to the two .dylib symlinks in order to avoid any compiling error:
$ install_name_tool -id "/opt/intel/mklml/lib/libmklml.dylib" /usr/local/lib/libmklml.dylib
$ install_name_tool -id "/opt/intel/mklml/lib/libiomp5.dylib" /usr/local/lib/libiomp5 .dylib
Download and extract Tensorflow:
$ wget https://github.com/tensorflow/tensorflow/archive/v1.3.1.tar.gz
$ tar -xzvf v1.3.1.tar.gz
Supposing new folder tensorflow-1.3.1 has been extracted:
$ cd tensorflow-1.3.1
Key changes to edit the file tensorflow-1.3.1/configure:
- Change MKL_ML_LIB_PATH to lib/libmklml.dylib
- Change MKL_ML_OMP_LIB_PATH to lib/libiomp5.dylib
- Comment out the parts that say "if linux:"
- Comment out the parts that say "Darwin is not supported" and "exit" clause
- Comment out the parts that deal with libdl.so.2
Changes to tensorflow-1.3.1/third_party/mkl/BUILD: Change the list of three .so files to the two .dylib files
Start configuring the source:
$ ./configure
Type Yes to use MKL, No to download MKL. MKL installed separately. Path to MKL: /opt/intel/mklml
Apple builtin LLVM (clang) doesn't support compiler option like "-fopenmp" so let's skip checking it by modifying tensorflow/tensorflow.bzl:
- Find the line:
]) + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
- Then remove the value "-fopenmp" from the statement:
]) + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1"]) + if_android_arm(
Build command:
$ cd tensorflow-1.3.1
$ bazel build -c opt --config=opt --config=mkl --copt="-DEIGEN_USE_VML" --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse4.1 --copt=-msse4.2 --linkopt="-Wl,-rpath,/opt/intel/mklml/lib" --linkopt="-L/opt/intel/mklml/lib" --linkopt="-lmklml" --linkopt="-iomp5" //tensorflow/tools/pip_package:build_pip_package
This build process lasted for more than half hour on my Mac.
Once the build process is completed, let's check the wheel out:
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Double check if the file is there:
$ ls -l /tmp/tensorflow_pkg/tensorflow-1.3.1-cp27-cp27m-macosx_10_12_intel.whl
Finally, use pip to install the wheel:
$ pip install --ignore-installed --upgrade /tmp/tensorflow_pkg/tensorflow-1.3.1-cp27-cp27m-macosx_10_12_intel.whl
After the installation, let's have a fast (10x-50x) implementation of protobuf to boost the performance:
(*This is also the solution to KeyError: "Couldn't find enum google.protobuf.MethodOptions.IdempotencyLevel" while running command like "import tensorflow" within python)
For Python 2.7:
pip install --upgrade \ https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.1.0-cp27-none-macosx_10_11_x86_64.whl
For Python 3.n:
$ pip3 install --upgrade \ https://storage.googleapis.com/tensorflow/mac/cpu/protobuf-3.1.0-cp35-none-macosx_10_11_x86_64.whl
To check if the Tensorflow is running properly, try the followings:
$ python
>>> import tensorflow as tf
>>>If no error message appears, then Tensorflow is imported successfully in Python.
Time to start your spiritual work in Deep Learning!