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[Doc] Add MKL install method apt/yum into tutorial #15491

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[Doc] Add MKL install method apt/yum into tutorial #15491

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0d83b61
add mkl install method
xinyu-intel Jul 9, 2019
3ddbd55
update in build instruction
xinyu-intel Jul 9, 2019
b1eae7e
trigger
xinyu-intel Jul 10, 2019
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5 changes: 3 additions & 2 deletions docs/install/build_from_source.md
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Expand Up @@ -123,8 +123,7 @@ You can set the BLAS library explicitly by setting the BLAS variable to:

See the [cmake/ChooseBLAS.cmake](https://github.com/apache/incubator-mxnet/blob/master/cmake/ChooseBlas.cmake) file for the options.

Intel's MKL (Math Kernel Library) is one of the most powerful math libraries
https://software.intel.com/en-us/mkl
[Intel's MKL (Math Kernel Library)](https://software.intel.com/en-us/mkl) is one of the most powerful math libraries

It has following flavors:

Expand All @@ -144,6 +143,8 @@ shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the [MKL-DNN
Since the full MKL library is almost always faster than any other BLAS library it's turned on by default,
however it needs to be downloaded and installed manually before doing `cmake` configuration.
Register and download on the [Intel performance libraries website](https://software.intel.com/en-us/performance-libraries).
You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo)
or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.

Note: MKL is supported only for desktop builds and the framework itself supports the following
hardware:
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2 changes: 1 addition & 1 deletion docs/tutorials/mkldnn/MKLDNN_README.md
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Expand Up @@ -214,7 +214,7 @@ With MKL BLAS, the performace is expected to furtherly improved with variable ra
You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
Installing the full MKL installation enables MKL support for all operators under the linalg namespace.

1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl)
1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl) You can also install MKL through [YUM](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-yum-repo) or [APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo) Repository.

2. Run `make -j ${nproc} USE_BLAS=mkl`

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