marcoabreu closed pull request #9810: remove MKL_EXPERIMENTAL and update make
files for MKL-DNN
URL: https://github.com/apache/incubator-mxnet/pull/9810
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diff --git a/Jenkinsfile b/Jenkinsfile
index 17d546c87f4..a20d9db545c 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -24,6 +24,7 @@
mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, dmlc-core/libdmlc.a,
nnvm/lib/libnnvm.a'
// mxnet cmake libraries, in cmake builds we do not produce a libnvvm static
library by default.
mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a,
build/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests,
build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a,
build/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests,
build/3rdparty/openmp/runtime/src/libomp.so,
build/3rdparty/mkldnn/src/libmkldnn.so,
build/3rdparty/mkldnn/src/libmkldnn.so.0'
mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libiomp5.so,
lib/libmklml_gnu.so, lib/libmkldnn.so, lib/libmkldnn.so.0,
lib/libmklml_intel.so, dmlc-core/libdmlc.a, nnvm/lib/libnnvm.a'
// command to start a docker container
docker_run = 'tests/ci_build/ci_build.sh'
@@ -260,6 +261,23 @@ try {
}
}
},
+ 'GPU: CMake MKLDNN': {
+ node('mxnetlinux-cpu') {
+ ws('workspace/build-cmake-mkldnn-gpu') {
+ init_git()
+ def defines = """ \
+ -DUSE_CUDA=1 \
+ -DUSE_CUDNN=1 \
+ -DUSE_MKLML_MKL=1 \
+ -DUSE_MKLDNN=1 \
+ -DCMAKE_BUILD_TYPE=Release \
+ """
+ def flag = "-v"
+ cmake("build_cuda", defines, flag)
+ pack_lib('cmake_mkldnn_gpu', mx_cmake_mkldnn_lib)
+ }
+ }
+ },
'GPU: CMake': {
node('mxnetlinux-cpu') {
ws('workspace/build-cmake-gpu') {
diff --git a/MKL_README.md b/MKL_README.md
index 0f97416ac36..5374adb8e42 100644
--- a/MKL_README.md
+++ b/MKL_README.md
@@ -17,46 +17,3 @@ Installing and enabling the full MKL installation enables
MKL support for all op
5. Run 'sudo python setup.py install'
-# MKL2017 PLUGIN
-
-MKL2017 is an INTEL released library to accelerate Deep Neural Network (DNN)
applications on Intel architecture.
-
-MKL2017_ML is a subset of MKL2017 and only contains DNN acceleration feature,
MKL2017 release cycle is longer then MKL2017_ML and MKL2017_ML support latest
feature
-
-This README shows the user how to setup and install MKL2017 library with mxnet.
-
-## Build/Install MXNet with MKL:
-
- 1. Enable USE_MKL2017=1 in make/config.mk
-
- 1.1 By default, MKL_2017_EXPRIEMENTAL=0. If setting
MKL_2017_EXPRIEMENTAL=1, MKL buffer will be created and transferred between
layers to achiever much higher performance.
-
- 1.2 By default, MKLML_ROOT=/usr/local, MKL2017_ML will be used
-
- 1.2.1 when excute make, Makefile will execute "prepare_mkl.sh" to
download the MKL2017_ML library under <MKLML_ROOT>
-
- 1.2.2 manually steps for download MKL2017_ML problem
-
- 1.2.2.1 wget
https://github.com/dmlc/web-data/raw/master/mxnet/mklml-release/mklml_lnx_<MKL
VERSION>.tgz
-
- 1.2.2.2 tar zxvf mklml_lnx_<MKL VERSION>.tgz
-
- 1.2.2.3 cp -rf mklml_lnx_<MKL VERSION>/* <MKLML_ROOT>/
-
- 1.2.3 Set LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MKLML_ROOT/lib
-
- 1.3 If setting USE_BLAS=mkl
-
- 1.3.1 mshadow can also utilize mkl blas function in mklml package
-
- 1.4 MKL version compatibility
-
- 1.3.2.1 If you already have MKL installed and MKLROOT being set in
your system, by default, it will not attempt to download the latest mklml
package unless you unset MKLROOT.
-
- 2. Run 'make -jX'
-
- 3. Navigate into the python directory
-
- 4. Run 'sudo python setup.py install'
-
-
diff --git a/docker_multiarch/arm.crosscompile.android.mk
b/docker_multiarch/arm.crosscompile.android.mk
index 36b8e9bed79..0302c5cf25a 100644
--- a/docker_multiarch/arm.crosscompile.android.mk
+++ b/docker_multiarch/arm.crosscompile.android.mk
@@ -82,21 +82,6 @@ USE_OPENCV = 0
# use openmp for parallelization
USE_OPENMP = 1
-# MKL ML Library for Intel CPU/Xeon Phi
-# Please refer to MKL_README.md for details
-
-# MKL ML Library folder, need to be root for /usr/local
-# Change to User Home directory for standard user
-# For USE_BLAS!=mkl only
-MKLML_ROOT=/usr/local
-
-# whether use MKL2017 library
-USE_MKL2017 = 0
-
-# whether use MKL2017 experimental feature for high performance
-# Prerequisite USE_MKL2017=1
-USE_MKL2017_EXPERIMENTAL = 0
-
# whether use NNPACK library
USE_NNPACK = 0
@@ -115,13 +100,10 @@ USE_LAPACK_PATH =
USE_INTEL_PATH = NONE
# If use MKL only for BLAS, choose static link automatically to allow python
wrapper
-ifeq ($(USE_MKL2017), 0)
+USE_STATIC_MKL = NONE
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
endif
-else
-USE_STATIC_MKL = NONE
-endif
#----------------------------
# distributed computing
diff --git a/docker_multiarch/arm.crosscompile.mk
b/docker_multiarch/arm.crosscompile.mk
index da0552eb2fb..2bca9e396d7 100644
--- a/docker_multiarch/arm.crosscompile.mk
+++ b/docker_multiarch/arm.crosscompile.mk
@@ -82,21 +82,6 @@ USE_OPENCV = 0
# use openmp for parallelization
USE_OPENMP = 1
-# MKL ML Library for Intel CPU/Xeon Phi
-# Please refer to MKL_README.md for details
-
-# MKL ML Library folder, need to be root for /usr/local
-# Change to User Home directory for standard user
-# For USE_BLAS!=mkl only
-MKLML_ROOT=/usr/local
-
-# whether use MKL2017 library
-USE_MKL2017 = 0
-
-# whether use MKL2017 experimental feature for high performance
-# Prerequisite USE_MKL2017=1
-USE_MKL2017_EXPERIMENTAL = 0
-
# whether use NNPACK library
USE_NNPACK = 0
@@ -115,13 +100,10 @@ USE_LAPACK_PATH =
USE_INTEL_PATH = NONE
# If use MKL only for BLAS, choose static link automatically to allow python
wrapper
-ifeq ($(USE_MKL2017), 0)
+USE_STATIC_MKL = NONE
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
endif
-else
-USE_STATIC_MKL = NONE
-endif
#----------------------------
# distributed computing
@@ -176,4 +158,4 @@ USE_CPP_PACKAGE = 0
# whether to use sframe integration. This requires build sframe
# g...@github.com:dato-code/SFrame.git
# SFRAME_PATH = $(HOME)/SFrame
-# MXNET_PLUGINS += plugin/sframe/plugin.mk
\ No newline at end of file
+# MXNET_PLUGINS += plugin/sframe/plugin.mk
diff --git a/docs/faq/perf.md b/docs/faq/perf.md
index 519959810f3..e021f1e9a21 100644
--- a/docs/faq/perf.md
+++ b/docs/faq/perf.md
@@ -18,10 +18,7 @@ Performance is mainly affected by the following 4 factors:
## Intel CPU
For using Intel Xeon CPUs for training and inference, we suggest enabling
-both `USE_MKL2017 = 1` and `USE_MKL2017_EXPERIMENTAL = 1` in
-`config.mk`. Check
-[MKL_README.md](https://github.com/dmlc/mxnet/blob/master/MKL_README.md) for
-details.
+`USE_MKLDNN = 1` in`config.mk`.
We also find that setting the following two environment variables can help:
- `export KMP_AFFINITY=granularity=fine,compact,1,0` if there are two physical
CPUs
diff --git a/example/image-classification/benchmark_score.py
b/example/image-classification/benchmark_score.py
index b6d1d642c86..82903b63238 100644
--- a/example/image-classification/benchmark_score.py
+++ b/example/image-classification/benchmark_score.py
@@ -70,7 +70,7 @@ def score(network, dev, batch_size, num_batches):
if __name__ == '__main__':
networks = ['alexnet', 'vgg-16', 'inception-bn', 'inception-v3',
'resnet-50', 'resnet-152']
devs = [mx.gpu(0)] if len(get_gpus()) > 0 else []
- # Enable USE_MKL2017_EXPERIMENTAL for better CPU performance
+ # Enable USE_MKLDNN for better CPU performance
devs.append(mx.cpu())
batch_sizes = [1, 2, 4, 8, 16, 32]
diff --git a/make/config.mk b/make/config.mk
index 1591d2ad60a..fa429f31f29 100644
--- a/make/config.mk
+++ b/make/config.mk
@@ -95,20 +95,8 @@ USE_LIBJPEG_TURBO_PATH = NONE
# use openmp for parallelization
USE_OPENMP = 1
-# MKL ML Library for Intel CPU/Xeon Phi
-# Please refer to MKL_README.md for details
-
-# MKL ML Library folder, need to be root for /usr/local
-# Change to User Home directory for standard user
-# For USE_BLAS!=mkl only
-MKLML_ROOT=/usr/local
-
-# whether use MKL2017 library
-USE_MKL2017 = 0
-
-# whether use MKL2017 experimental feature for high performance
-# Prerequisite USE_MKL2017=1
-USE_MKL2017_EXPERIMENTAL = 0
+# whether use MKL-DNN library
+USE_MKLDNN = 0
# whether use NNPACK library
USE_NNPACK = 0
diff --git a/make/osx.mk b/make/osx.mk
index 624677966e1..47f395bccf9 100644
--- a/make/osx.mk
+++ b/make/osx.mk
@@ -88,7 +88,7 @@ USE_BLAS = apple
USE_LAPACK = 1
# by default, disable lapack when using MKL
-# switch on when there is a full installation of MKL available (not just
MKL2017/MKL_ML)
+# switch on when there is a full installation of MKL available (not just
MKL_ML)
ifeq ($(USE_BLAS), mkl)
USE_LAPACK = 0
endif
diff --git a/tests/python/cpu/test_mklml.py b/tests/python/cpu/test_mkldnn.py
similarity index 81%
rename from tests/python/cpu/test_mklml.py
rename to tests/python/cpu/test_mkldnn.py
index decd5b1c985..16b30039737 100644
--- a/tests/python/cpu/test_mklml.py
+++ b/tests/python/cpu/test_mkldnn.py
@@ -16,24 +16,24 @@
# under the License.
"""
-MKLML related test cases
+MKL-DNN related test cases
"""
import logging
import os
from sys import platform
-def test_mklml_install():
+def test_mkldnn_install():
"""
This test will verify that MXNet is built/installed correctly when
- compiled with Intel MKLML library. The method will try to import
- the mxnet module and see if the mklml library is mapped to this
+ compiled with Intel MKL-DNN library. The method will try to import
+ the mxnet module and see if the mkldnn library is mapped to this
process's address space.
"""
logging.basicConfig(level=logging.INFO)
if not platform.startswith('linux'):
- logging.info("Bypass mklml install test for non-Linux OS")
+ logging.info("Bypass mkldnn install test for non-Linux OS")
return
try:
@@ -45,14 +45,14 @@ def test_mklml_install():
pid = os.getpid()
rc = os.system("cat /proc/" + str(pid) + \
- "/maps | grep libmklml_ > /dev/null")
+ "/maps | grep libmkldnn > /dev/null")
if rc == 0:
- logging.info("MXNet is built/installed correctly with MKLML")
+ logging.info("MXNet is built/installed correctly with MKL-DNN")
else:
- assert 0, "MXNet is built/installed incorrectly with MKLML, please " \
+ assert 0, "MXNet is built/installed incorrectly with MKL-DNN, please "
\
"double check your build/install steps or environment " \
"variable settings"
if __name__ == '__main__':
- test_mklml_install()
+ test_mkldnn_install()
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