Deeplearning4j

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. 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). Deeplearning4J has quite a few dependencies. For this reason we only support usage with a build tool. 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. 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. For users looking for being able to run models from other frameworks, see: 1. Onnx: 2. Tensorflow/Keras: 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: Various instructions for cpu and gpu builds can be found there. Please go to our forums for further help. 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.