TensorFlow can run on any JVM for building, training and running machine learning models. It comes with a series of utilities and frameworks that help achieve most of the tasks common to data scientists and developers working in this domain. Java and other JVM languages, such as Scala or Kotlin, are frequently used in small-to-large enterprises all over the world, which makes TensorFlow a strategic choice for adopting machine learning at a large scale.
In the early days, the Java language bindings for TensorFlow were hosted in the main repository and released only when a new version of the core library was ready to be distributed, which happens only a few times a year. Now, all Java-related code has been moved to this repository so that it can evolve and be released independently from official TensorFlow releases. In addition, most of the build tasks have been migrated from Bazel to Maven, which is more familiar for most Java developers.
The following describes the layout of the repository and its different artifacts:
-
tensorflow-core- All artifacts that build up the core language bindings of TensorFlow for Java
- Intended audience: projects that provide their own APIs or frameworks on top of TensorFlow and just want a thin layer to access the TensorFlow runtime from the JVM
-
tensorflow-framework- Primary API for building and training neural networks with TensorFlow
- Intended audience: neural network developers
- For more information: tensorflow-framework/README.md
Note: The NdArray Library module has now its own repository and has been moved out of TensorFlow Java.
This repository is maintained by TensorFlow JVM Special Interest Group (SIG). You can easily join the group by subscribing to the jvm@tensorflow.org mailing list, or you can simply send pull requests and raise issues to this repository. There is also a sig-jvm Gitter channel.
See CONTRIBUTING.md.
To include TensorFlow in your Maven application, you first need to add a dependency on either the
tensorflow-core or tensorflow-core-platform artifacts. The former could be included multiple times
for different targeted systems by their classifiers, while the later includes them as dependencies for
linux-x86_64, macosx-x86_64, and windows-x86_64, with more to come in the future. There are also
tensorflow-core-platform-mkl, tensorflow-core-platform-gpu, and tensorflow-core-platform-mkl-gpu
artifacts that depend on artifacts with MKL and/or CUDA support enabled.
For example, for building a JAR that uses TensorFlow and is targeted to be deployed only on Linux systems, you should add the following dependencies:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-api</artifactId>
<version>0.4.1</version>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-api</artifactId>
<version>0.4.1</version>
<classifier>linux-x86_64${javacpp.platform.extension}</classifier>
</dependency>On the other hand, if you plan to deploy your JAR on more platforms, you need additional native dependencies as follows:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-api</artifactId>
<version>0.4.1</version>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-api</artifactId>
<version>0.4.1</version>
<classifier>linux-x86_64${javacpp.platform.extension}</classifier>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-api</artifactId>
<version>0.4.1</version>
<classifier>macosx-x86_64${javacpp.platform.extension}</classifier>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-api</artifactId>
<version>0.4.1</version>
<classifier>windows-x86_64${javacpp.platform.extension}</classifier>
</dependency>In some cases, pre-configured starter artifacts can help to automatically include all versions of
the native library for a given configuration. For example, the tensorflow-core-platform,
tensorflow-core-platform-mkl, tensorflow-core-platform-gpu, or tensorflow-core-platform-mkl-gpu
artifact includes transitively all the artifacts above as a single dependency:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-platform${javacpp.platform.extension}</artifactId>
<version>0.4.1</version>
</dependency>Be aware though that the native library is quite large and including too many versions of it may
significantly increase the size of your JAR. So it is good practice to limit your dependencies to
the platforms you are targeting. For this purpose the -platform artifacts include profiles that follow
the conventions established on this page:
Snapshots of TensorFlow Java artifacts are automatically distributed after each update in the code. To use them, you need to add Sonatype OSS repository in your pom.xml, like the following
<repositories>
<repository>
<id>tensorflow-snapshots</id>
<url>https://oss.sonatype.org/content/repositories/snapshots/</url>
<snapshots>
<enabled>true</enabled>
</snapshots>
</repository>
</repositories>
<dependencies>
<!-- Example of dependency, see section above for more options -->
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-core-platform</artifactId>
<version>0.5.0-SNAPSHOT</version>
</dependency>
</dependencies>This table shows the mapping between TensorFlow, TensorFlow Java and minimum supported Java versions.
| TensorFlow Java Version | TensorFlow Version | Minimum Java Version |
|---|---|---|
| 0.2.0 | 2.3.1 | 8 |
| 0.3.0 | 2.4.1 | 8 |
| 0.3.1 | 2.4.1 | 8 |
| 0.3.2 | 2.4.1 | 8 |
| 0.3.3 | 2.4.1 | 8 |
| 0.4.0 | 2.7.0 | 8 |
| 0.4.1 | 2.7.1 | 8 |
| 0.5.0-SNAPSHOT | 2.8.0 | 11 |
Contributions are welcome, guidelines are located in CONTRIBUTING.md.