[incubator-mxnet] branch master updated: [Doc] Add MKL install method apt/yum into tutorial (#15491)

[pa...@apache.org]

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patriczhao pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
     new 68460a9  [Doc] Add MKL install method apt/yum into tutorial (#15491)
68460a9 is described below

commit 68460a927dceef5dfbd5d8db0550df1530ef57fd
Author: Xinyu Chen <xi...@intel.com>
AuthorDate: Thu Jul 11 11:29:36 2019 +0800

    [Doc] Add MKL install method apt/yum into tutorial (#15491)
    
    * add mkl install method
    
    * update in build instruction
    
    * trigger
---
 docs/install/build_from_source.md      | 5 +++--
 docs/tutorials/mkldnn/MKLDNN_README.md | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md
index b47267d..6fbfa24 100644
--- a/docs/install/build_from_source.md
+++ b/docs/install/build_from_source.md
@@ -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:
 
@@ -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:
diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
index 460fa20..516b2b3 100644
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -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`