Mkl Library Mac
Intel Math Kernel Library (Intel MKL) is a library of optimized math routines for science, engineering, and financial applications. Wikipedia ( Intel MKL include BLAS, LAPACK, FFT,. Support for Mac OS is now here! With Intel MKL for Mac OS, you can easily port your applications to Mac OS by using the same library API and functions available for Windows and Linux. Development Environments Intel MKL is easily used and integrated with popular development tools and environments. 本文中libiomp5，libgomp，libomp分别是intel、gnu和clang的OpenMP runtime library -因为mac系统的谜之SB设定，在mac上开发需要调用MKL的OpenMP并行计算程序（同时需要兼.
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.
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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.
Here's the build instruction:
System: Mac OS X 10.12.6
Mkl Intel
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
If no error message appears, then Tensorflow is imported successfully in Python.
Time to start your spiritual work in Deep Learning!
Posted on March 24, 2017 by Paul
In this article, I will give you a quick introduction in how to get started with Armadillo, a C++ Matlab like Linear Algebra Library on Windows, Mac and Linux. Armadillo is particularly interesting for Matlab users that want to port their code to C++ for speed. Please note that Armadillo is not a drop in replacement for all Matlab functionality. If your code uses some specialized Matlab toolbox, you will need to find another library to complement Armadillo or implement the missing functionality from scratch.
Armadillo uses BLAS and LAPACK for matrix operations. It is recommended that you install an optimized version of the above libraries on your system and not the reference implementations from Netlib. I will guide you on how to install accelerated versions of BLAS and LAPACK for your operating system, in the install sections of this article.
Simple Armadillo test program:
Let’s start with a simple example of using Armadillo. Please note the Matlab like high level syntax:
This is the result of the above code on my machine:
If you need to transfer data between Matlab and Armadillo check my other Armadillo article.
If you want to have a taste of how elaborate is to directly use functions from BLAS or LAPACK, check the documentation for the reference implementation of SGEMM which performs matrix-matrix multiplication. Alternatively, you can check Intel’s documentation page for their C wrapper for SGEMM.
Next, go to the section that matches your OS: Windows, Mac or Linux.
Windows installation:How to clean out library folder mac.
I assume that you have latest stable version of Visual Studio installed, currently this is 2017, and that you can create, compile and execute a simple C++ code from the IDE. If you can’t find the C++ project template when you open VS, make sure to install the C++ support. In the latest versions of VS, this is not installed by default.
On Windows, I would recommend using Intel MKL if you have an Intel processor or OpenBlas which works on both Intel and AMD processors. Both alternatives provide Windows binaries and the usage is similar, you need to link the libraries to your compiled code. I will exemplify the process using Intel MKL, which I personally prefer.
Intel MKL can be freely obtained from the above link, by creating an Intel account. After registration, you will be able to download an installer which will copy the libraries, typically in C:Program Files (x86)IntelSWTools. Open IntelSWTools and find the mkl folder. For example, on my machine, I found mkl in IntelSWT → compilers_and_libraries_xxxxx → windows.
Next, create a new Visual C++ → Win32 Console Application project. Be sure to select Empty Project, like in the next image:
Right click on the Source Files line and add a C++ source file. I named this main.cpp, but you can use any name you want:
Now, open the project folder. By default, VS will save new projects in Documents → Visual Studio 2xxx → Projects. I’ve named my project Armadillo_demo, so I will open this folder. Copy the mkl folder in your project folder.
Download Intel Mkl
Download the stable version of Armadillo from http://arma.sourceforge.net/download.html, at the time of this writing this is armadillo-7.800.2.tar.xz. Extract the archive, I’ve used 7zip for this. Once the archive is extracted. open the armadillo-xxx folder and copy the include folder in your project folder, like you did with mkl. I’ve put the include folder in my project folder inside a new folder named armadillo.
If you’ve followed my instructions, you should have something like this in your project folder:
Please note that armadillo from the above image contains the include folder from armadillo-xxx.
You can fine tune Armadillo, by modifying config.hpp from armadillo → include → armadillo_bits. For the purposes of this tutorial, we’ll keep the defaults. As a side note, config.hpp is a header file, so you can modify it at any time, preferably when you have more experience with Armadillo.
The reason I’ve directed you to copy the above folders in your project folder, is to have a self contained project. Basically, you will be able to move the project on a different computer that has VS installed and use the project as it is.
In the future, if you need/want to update Armadillo, simply replace the files from the above include folder.
First open the project in VS, if it is not already opened. Right click on the project name and select Properties. Unfold the C/C++ list and select General → Additional Include Directories, double click and select Edit. Use the yellow button to insert a new line and browse for mklinclude in your project folder and press Select folder. Click on the path line and replace everything until mklinclude with two dots. You should have something like .mklinclude. Repeat the above steps for armadilloinclude. You should end up with something like:
Press OK.
Next, click on Linker → General → Additional Import Library and select mkllibintel64_win. In the unlikely case that your computer or Windows is 32 bits, use mkllibia32_win. Like before, erase the first part of the path and replace it with two dots. You should end up with something like:
Press OK.
Now, click on Linker → Input → Additional Dependencies, click on the existing line and select Edit. Add three new entries: mkl_core.lib, mkl_sequential.lib, mkl_intel_lp64.lib:
Press OK on the above window. Press Apply and OK on the Properties window.
Now, copy the Armadillo example from the beginning of this article, in your main.cpp file.
Change the compiler to x64:
Now, build and run the code by pressing Debug → Start Without Debugging. You should see something like this:
Intel Math Kernel Library Mkl
Mac installation:
On Mac, first make sure that you have the latest versions of Xcode and Command Line Tools installed. You can install Xcode from the AppStore application. If you don’t have the Command Line Tools installed, open a Terminal and type:
which will guide you through the installation process.
Once Xcode is installed, open the application and accept the license (without this, some people reported strange errors).
Next, install Homebrew, which is a nice package manager that will let you install various software from your Terminal. Just follow the installation instructions from their web page and accept the defaults.
Go to the Armadillo web page and check the version number of the stable version. Usually, Homebrew has the latest version of Armadillo available. If this is not the case you can install from sources.
Open a Terminal and type:
If the version number for Armadillo reported by Homebrew, corresponds to the stable version from the library web page, all is good. You can install the library with:
If the Hombrew version is older than the latest stable version of Armadillo, download the library and extract the archive in your Downloads folder. In order to compile Armadillo from sources, open a Terminal and write:
At this point, you should have Armadillo installed on your machine. On Mac, there is no need to install BLAS and LAPACK, the system already provides accelerated versions of these libraries and Armadillo will use them.
First, I will show you how to compile the example from the beginning of this article, using the Terminal. Copy the code in a file named test.cpp and write:
Running the above commands on my machine:
Next, open Xcode and create a new macOS, Command Line Tool project. Give the project a name, something like Armadillo_demo. Make sure that you select C++ for the Language:
press Next and accept the defaults for the remaining options.
In the left panel, click on the project name and select the Build Settings tab. Next, use the search box to find the compiler search paths for headers and libraries. Click on the Header Search Paths and add /usr/local/include. Repeat for the Library Search Paths and add /usr/local/lib:
Next, select the Build Phases tab → Link Binary With Libraries, press + and select Add Other. Now, press Command + Shift + G which will open a search text box, write /usr/local and press Go. Select the lib folder and libarmadillo.dylib. Press Open to finish:
An alternative approach to add the library, is to use the Terminal to navigate to /usr/local/lib. Open Finder directly in /usr/local/lib and simply drag the library file libarmadillo.dylib in Xcode:
Mkl Library Mac Torrent
Now, copy the Armadillo example from the beginning of this article, to your main.cpp file and press the run button:
Linux installation:
I will use Ubuntu 16.04 LTS to exemplify the Linux installation.
Open a Terminal and make sure that your system is updated:
Next, we’ll install Armadillo prerequisites:
Theoretically, you can install Armadillo using the apt package manager, but this is not recommended because the version provided by apt is really old. I suggest to download and extract the latest stable release of Armadillo. Once you have Armadillo extracted in your Downloads folder, you can build and install the library with:
Now, copy the Armadillo example from the beginning of this article, to a file named test.cpp. You can compile the code using:
Running the code on my system:
Mkl Library Macon Ga
If you are interested to learn more about the new C++11/C++14 syntax, I would recommend reading The C++ Programming Language by Bjarne Stroustrup.
or, Professional C++ by M. Gregoire, N. A. Solter, S. J. Kleper: