From 0d83b61163b50ab85b19664f6492f8a51cf7b3ad Mon Sep 17 00:00:00 2001 From: xinyu Date: Tue, 9 Jul 2019 11:48:00 +0800 Subject: [PATCH 1/3] add mkl install method --- docs/tutorials/mkldnn/MKLDNN_README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md index 460fa200cc46..516b2b3e796a 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` From 3ddbd55a40cc3d9ba6d22168672618c41eb2e61e Mon Sep 17 00:00:00 2001 From: xinyu Date: Tue, 9 Jul 2019 13:10:05 +0800 Subject: [PATCH 2/3] update in build instruction --- docs/install/build_from_source.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md index b47267db8ef5..6fbfa247c037 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: From b1eae7ebef3a4101bd0d4426ada77ac631ff5243 Mon Sep 17 00:00:00 2001 From: xinyu Date: Wed, 10 Jul 2019 13:55:18 +0800 Subject: [PATCH 3/3] trigger