WARNING: The TensorFlow Java API is not currently covered by the TensorFlow API stability guarantees.
For using TensorFlow on Android refer instead to TensorFlow Lite.
Releases built from release branches are available on Maven Central.
Additionally, every day binaries are built from the master
branch on GitHub:
If the quickstart instructions above do not work out, the TensorFlow Java and native libraries will need to be built from source.
Install bazel
Setup the environment to build TensorFlow from source code (Linux or macOS). If you'd like to skip reading those details and do not care about GPU support, try the following:
# On Linux
sudo apt-get install python swig python-numpy
# On Mac OS X with homebrew
brew install swig
Configure (e.g., enable GPU support) and build:
./configure
bazel build --config opt \
\
//tensorflow/java:tensorflow //tensorflow/java:libtensorflow_jni
The command above will produce two files in the
bazel-bin/tensorflow/java
directory:
libtensorflow.jar
libtensorflow_jni.so
on Linux,
libtensorflow_jni.dylib
on OS X, or
tensorflow_jni.dll
on Windows.To compile Java code that uses the TensorFlow Java API, include
libtensorflow.jar
in the classpath. For example:
javac -cp bazel-bin/tensorflow/java/libtensorflow.jar ...
To execute the compiled program, include
libtensorflow.jar
in the classpath and the native library
in the library path. For example:
java -cp bazel-bin/tensorflow/java/libtensorflow.jar \
-Djava.library.path=bazel-bin/tensorflow/java \
...
Installation on Windows requires the more experimental bazel on
Windows. Details are omitted here, but find inspiration in the
script used for building the release archive: tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh
.
If your project uses bazel for builds, add a dependency on
//tensorflow/java:tensorflow
to the
java_binary
or java_library
rule. For
example:
bazel run -c opt //tensorflow/java/src/main/java/org/tensorflow/examples:label_image