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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).

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-beta7</version>
  </dependency>
  <dependency>
      <groupId>org.nd4j</groupId>
      <artifactId>nd4j-native-platform</artifactId>
      <version>1.0.0-beta7</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.

A taste of code

Deeplearning4J offers a very high level API for defining even complex neural networks. The following example code shows you how LeNet, a convolutional neural network, is defined in DL4J.

MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .l2(0.0005)
                .weightInit(WeightInit.XAVIER)
                .updater(new Adam(1e-3))
                .list()
                .layer(new ConvolutionLayer.Builder(5, 5)
                        .stride(1,1)
                        .nOut(20)
                        .activation(Activation.IDENTITY)
                        .build())
                .layer(new SubsamplingLayer.Builder(PoolingType.MAX)
                        .kernelSize(2,2)
                        .stride(2,2)
                        .build())
                .layer(new ConvolutionLayer.Builder(5, 5)
                        .stride(1,1)
                        .nOut(50)
                        .activation(Activation.IDENTITY)
                        .build())
                .layer(new SubsamplingLayer.Builder(PoolingType.MAX)
                        .kernelSize(2,2)
                        .stride(2,2)
                        .build())
                .layer(new DenseLayer.Builder().activation(Activation.RELU)
                        .nOut(500).build())
                .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nOut(outputNum)
                        .activation(Activation.SOFTMAX)
                        .build())
                .setInputType(InputType.convolutionalFlat(28,28,1))
                .build();

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/getting-started/build-from-source.

To build everything, we can use commands like

./change-cuda-versions.sh x.x
./change-scala-versions.sh 2.xx
./change-spark-versions.sh x
mvn clean install -Dmaven.test.skip -Dlibnd4j.cuda=x.x -Dlibnd4j.compute=xx

or

mvn -B -V -U clean install -pl '!jumpy,!pydatavec,!pydl4j' -Dlibnd4j.platform=linux-x86_64 -Dlibnd4j.chip=cuda -Dlibnd4j.cuda=9.2 -Dlibnd4j.compute=<your GPU CC> -Djavacpp.platform=linux-x86_64 -Dmaven.test.skip=true

An example of GPU "CC" or compute capability is 61 for Titan X Pascal.

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.