CentOS 6 编译 TensorFlow for Java 以及 Maven Pom
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我们的系统环境
CentOS 6.5, JDK 1.8 -
更新yum源
$ yum update -
安装 Python 2.7
$ yum install python27 python27-numpy python27-python-devel python27-python-wheel -
升级 gcc 至 4.8.2
$ cd /opt/
$ wget http://people.centos.org/tru/devtools-2/devtools-2.repo -O /etc/yum.repos.d/devtools-2.repo
$ yum install --nogpg -y zip unzip patch libcurl-devel git devtoolset-2-gcc devtoolset-2-binutils devtoolset-2-gcc-c++
$ scl enable devtoolset-2 python27 bash
$ gcc -v -
JDK版本
1.8（如1.8.0_73） -
安装bazel(编译不同版本的tensorflow，需要不同版本的bazel。这里编译tensorflow_v1.6.0，需要bazel_0.9.0)
$ cd /opt
$ wget https://github.com/bazelbuild/bazel/releases/download/0.9.0/bazel-0.9.0-dist.zip
$ unzip bazel-0.9.0-dist.zip -d bazel-0.9.0-dist
$ cd bazel-0.9.0-dist
$ ./compile.sh
$ mkdir -p ~/bin
$ cp output/bazel ~/bin/ -
编译tensorflow
$ cd /opt
$ git clone https://github.com/tensorflow/tensorflow && cd tensorflow
$ git checkout v1.6.0
$ ./configure
配置中提示的问题，全部选择n(no)
在执行bazel build之前，
把文件/opt/tensorflow/tensorflow/tensorflow.bzl中的代码片段
def tf_cc_shared_object(
name,
srcs=[],
deps=[],
linkopts=[],
framework_so=tf_binary_additional_srcs(),
**kwargs):
修改为
def tf_cc_shared_object(
name,
srcs=[],
deps=[],
linkopts=['-lrt'],
framework_so=tf_binary_additional_srcs(),
**kwargs):
把文件/opt/tensorflow/tensorflow/java/BUILD中的代码片段
tf_cc_binary(
name = "generate_pom",
srcs = ["generate_pom.cc"],
deps = ["//tensorflow/c:c_api"],
)
修改为
tf_cc_binary(
linkopts = ["-lrt"],
name = "generate_pom",
srcs = ["generate_pom.cc"],
deps = ["//tensorflow/c:c_api"],
)
把
tf_cc_binary(
name = "java_op_gen_tool",
srcs = [
"src/gen/cc/op_gen_main.cc",
],
copts = tf_copts(),
linkopts = select({
"//tensorflow:windows": [],
"//conditions:default": ["-lm"],
}),
linkstatic = 1,
deps = [
":java_op_gen_lib",
"//tensorflow/core:framework",
"//tensorflow/core:framework_internal",
"//tensorflow/core:lib",
"//tensorflow/core:ops",
],
)
修改为
tf_cc_binary(
name = "java_op_gen_tool",
srcs = [
"src/gen/cc/op_gen_main.cc",
],
copts = tf_copts(),
linkopts = select({
"//tensorflow:windows": [],
"//conditions:default": ["-lm","-lrt"],
}),
linkstatic = 1,
deps = [
":java_op_gen_lib",
"//tensorflow/core:framework",
"//tensorflow/core:framework_internal",
"//tensorflow/core:lib",
"//tensorflow/core:ops",
],
)
如果还是报错`ndefined reference to 'clock_gettime'`, 还需要把文件/opt/tensorflow/tensorflow/tensorflow.bzl中出现`linkopts`的地方，全部添加`'-lrt'`。
$ bazel build --linkopt='-lrt' -c opt //tensorflow/java:tensorflow //tensorflow/java:libtensorflow_jni //tensorflow/java:pom
$ cd /opt/tensorflow/tensorflow/java/maven/
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部署 libtensorflow
修改 /opt/tensorflow/bazel-bin/tensorflow/java/pom.xml 中的groupId, version用于deploy到自己公司的远程仓库中，并指定仓库的repositoryId(比如我的是artifactory)和url(请查看你的
配置) $ mvn deploy:deploy-file -Dfile=../../../bazel-bin/tensorflow/java/libtensorflow.jar -DpomFile=../../../bazel-bin/tensorflow/java/pom.xml -DrepositoryId= -Durl=
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部署 libtensorflow_jni
修改/opt/tensorflow/tensorflow/java/maven/pom.xml和/opt/tensorflow/tensorflow/java/maven/libtensorflow_jni/pom.xml中的groupId, version，并添加自己公司的distributionManagement配置，同时只需保留libtensorflow_jni模块。
$ cd /opt/tensorflow/tensorflow/java/maven/
$ mkdir -p libtensorflow_jni/src/main/resources/org/tensorflow/native/linux-x86_64
$ cp ../../../bazel-bin/tensorflow/libtensorflow_framework.so libtensorflow_jni/src/main/resources/org/tensorflow/native/linux-x86_64/
$ cp ../../../bazel-bin/tensorflow/java/libtensorflow_jni.so libtensorflow_jni/src/main/resources/org/tensorflow/native/linux-x86_64/
$ mvn versions:set -DnewVersion="${TENSORFLOW_VERSION}-cpu-optimized"
$ mvn package -Dgpg.skip=true
$ mvn deploy -Dgpg.skip=true -
参考资料
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附录: 编译成python 源
scl enable devtoolset-2 bash
bazel clean
./configure
bazel build --linkopt='-lrt' -c opt --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx --copt=-mavx2 --copt=-mfma //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc0-cp27-none-linux_x86_64.whl
