add mkl lib

tests/nightly/JenkinsfileForBinaries

@@ -20,7 +20,7 @@
 
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
-mx_lib_cpp_example = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference'
+mx_lib_cpp_example_mkl = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, lib/libtvmop.so, 3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, build/cpp-package/example/imagenet_inference, lib/libmkldnn.so.0, lib/libmklml_intel.so'
 
 node('utility') {
   // Loading the utilities requires a node context unfortunately
@@ -37,7 +37,7 @@ core_logic: {
         ws('workspace/build-gpu') {
           utils.init_git()
           utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_cuda101_cudnn7', false)
-          utils.pack_lib('gpu', mx_lib_cpp_example)
+          utils.pack_lib('gpu', mx_lib_cpp_example_mkl)
         }
       }
     }/*,
@@ -73,7 +73,7 @@ core_logic: {
     'ImageNet Inference: GPU': {
       node(NODE_LINUX_GPU) {
         ws('workspace/nt-ImageInferenceTest') {
-          utils.unpack_and_init('gpu', mx_lib_cpp_example)
+          utils.unpack_and_init('gpu', mx_lib_cpp_example_mkl)
           utils.docker_run('ubuntu_nightly_gpu', 'nightly_test_imagenet_inference', true)
         }
       }