Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
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).
For support for the project, please go over to https://community.konduit.ai/
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-M1.1</version> </dependency> <dependency> <groupId>org.nd4j</groupId> <artifactId>nd4j-native-platform</artifactId> <version>1.0.0-M1.1</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.
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();
You can find the official documentation for Deeplearning4J and the other libraries of its ecosystem at http://deeplearning4j.konduit.ai/.
We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples
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.
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
mvn -B -V -U clean install -pl -Dlibnd4j.platform=linux-x86_64 -Dlibnd4j.chip=cuda -Dlibnd4j.cuda=11.0 -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.
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 [email protected]