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The Eclipse Deeplearning4J (DL4J) ecosystem is a set of projects intended to support all the needs of a JVM based deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.

Because Deeplearning4J runs on the JVM you can use it with a wide variety of JVM based languages other than Java, like Scala, Kotlin, Clojure and many more.

The DL4J stack comprises of:

All projects in the DL4J ecosystem support Windows, Linux and macOS. Hardware support includes CUDA GPUs (10.0, 10.1, 10.2 except OSX), x86 CPU (x86_64, avx2, avx512), ARM CPU (arm, arm64, armhf) and PowerPC (ppc64le).

Community Support

For support for the project, please go over to https://community.konduit.ai/

Using Eclipse Deeplearning4J in your project

Deeplearning4J has quite a few dependencies. For this reason we only support usage with a build tool.

<dependencies>
  <dependency>
      <groupId>org.deeplearning4j</groupId>
      <artifactId>deeplearning4j-core</artifactId>
      <version>1.0.0-M2.1</version>
  </dependency>
  <dependency>
      <groupId>org.nd4j</groupId>
      <artifactId>nd4j-native-platform</artifactId>
      <version>1.0.0-M2.1</version>
  </dependency>
</dependencies>

Add these dependencies to your pom.xml file to use Deeplearning4J with the CPU backend. A full standalone project example is available in the example repository, if you want to start a new Maven project from scratch.

Code samples

Due to DL4J being a multi faceted project with several modules in the mono repo, we recommend looking at the examples for a taste of different usages of the different modules. Below we'll link to examples for each module.

  1. ND4J: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/nd4j-ndarray-examples
  2. DL4J: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/dl4j-examples
  3. Samediff: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/samediff-examples
  4. Datavec: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/data-pipeline-examples
  5. Python4j: https://deeplearning4j.konduit.ai/python4j/tutorials/quickstart

For users looking for being able to run models from other frameworks, see:

  1. Onnx: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/onnx-import-examples
  2. Tensorflow/Keras: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/tensorflow-keras-import-examples

Documentation, Guides and Tutorials

You can find the official documentation for Deeplearning4J and the other libraries of its ecosystem at http://deeplearning4j.konduit.ai/.

Want some examples?

We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples

Building from source

It is preferred to use the official pre-compiled releases (see above). But if you want to build from source, first take a look at the prerequisites for building from source here: https://deeplearning4j.konduit.ai/multi-project/how-to-guides/build-from-source. Various instructions for cpu and gpu builds can be found there. Please go to our forums for further help.

Running tests

In order to run tests, please see the platform-tests module. This module only runs on jdk 11 (mostly due to spark and bugs with older scala versions + JDK 17)

platform-tests allows you to run dl4j for different backends. There are a few properties you can specify on the command line:

  1. backend.artifactId: this defaults to nd4j-native and will run tests on cpu,you can specify other backends like nd4j-cuda-11.6
  2. dl4j.version: You can change the dl4j version that the tests run against. This defaults to 1.0.0-SNAPSHOT.

More parameters can be found here: https://github.com/deeplearning4j/deeplearning4j/blob/c1bf8717e4839c8930e9c43183bf7b94d0cf84dc/platform-tests/pom.xml#L47

Running project in Intellij IDEA:

  1. Ensure you follow https://stackoverflow.com/questions/45370178/exporting-a-package-from-system-module-is-not-allowed-with-release on jdk 9 or later
  2. Ignore all nd4j-shade submodules. Right click on each folder and click: Maven -> Ignore project

License

Apache License 2.0

Commercial Support

Deeplearning4J is actively developed by the team at Konduit K.K..

[If you need any commercial support feel free to reach out to us. at support@konduit.ai