1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
|
# TensorFlow for Java
> *WARNING*: The TensorFlow Java API is not currently covered by the TensorFlow
> [API stability guarantees](https://www.tensorflow.org/guide/version_semantics).
>
> For using TensorFlow on Android refer instead to
> [contrib/android](https://www.tensorflow.org/code/tensorflow/contrib/android),
> [makefile](https://www.tensorflow.org/code/tensorflow/contrib/makefile#android)
> and/or the [Android demo](https://www.tensorflow.org/code/tensorflow/examples/android).
## Quickstart
- Refer to [Installing TensorFlow for Java](https://www.tensorflow.org/install/lang_java)
- [Javadoc](https://www.tensorflow.org/api_docs/java/reference/org/tensorflow/package-summary)
- [](https://maven-badges.herokuapp.com/maven-central/org.tensorflow/tensorflow)
## Building from source
If the quickstart instructions above do not work out, the TensorFlow Java and
native libraries will need to be built from source.
1. Install [bazel](https://www.bazel.build/versions/master/docs/install.html)
2. Setup the environment to build TensorFlow from source code
([Linux or macOS](https://www.tensorflow.org/install/source)).
If you'd like to skip reading those details and do not care about GPU
support, try the following:
```sh
# On Linux
sudo apt-get install python swig python-numpy
# On Mac OS X with homebrew
brew install swig
```
3. [Configure](https://www.tensorflow.org/install/source)
(e.g., enable GPU support) and build:
```sh
./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:
* An archive of Java classes: `libtensorflow.jar`
* A native library: `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:
```sh
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:
```sh
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](https://bazel.build/versions/master/docs/windows.html). Details are
omitted here, but find inspiration in the script used for building the release
archive:
[`tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh`](https://www.tensorflow.org/code/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh).
### Maven
Details of the release process for Maven Central are in
[`maven/README.md`](https://www.tensorflow.org/code/tensorflow/java/maven/README.md).
However, for development, you can push the library built from source to a local
Maven repository with:
```sh
bazel build -c opt //tensorflow/java:pom
mvn install:install-file \
-Dfile=../../bazel-bin/tensorflow/java/libtensorflow.jar \
-DpomFile=../../bazel-bin/tensorflow/java/pom.xml
```
And then refer to this library in a project's `pom.xml` with: (replacing
VERSION with the appropriate version of TensorFlow):
```xml
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>libtensorflow</artifactId>
<version>VERSION</version>
</dependency>
```
### Bazel
If your project uses bazel for builds, add a dependency on
`//tensorflow/java:tensorflow` to the `java_binary` or `java_library` rule. For
example:
```sh
bazel run -c opt //tensorflow/java/src/main/java/org/tensorflow/examples:label_image
```
|
