Deeplearning4J is an Apache 2.0-licensed, open-source, distributed neural net library written in Java and Scala. By contributing code to this repository, you agree to make your contribution available under an Apache 2.0 license.

Deeplearning4J integrates with Hadoop and Spark and runs on several backends that enable use of CPUs and GPUs. The aim is to create a plug-and-play solution that is more convention than configuration, and which allows for fast prototyping.

The most recent stable release in Maven Central is 0.8.0, and the current master is 0.8.1-SNAPSHOT.

To get started using Deeplearning4j, please go to our Quickstart. You'll need to be familiar with a Java automated build tool such as Maven and an IDE such as IntelliJ.

• Versatile n-dimensional array class
• GPU integration(Supports devices starting from Kepler,cc3.0. You can check your device's compute compatibility here.)

• datavec = Library for converting images, text and CSV data into format suitable for Deep Learning
• nn = core neural net structures MultiLayer Network and Computation graph for designing Neural Net structures
• core = additional functionality building on deeplearning4j-nn
• modelimport = functionality to import models from Keras
• nlp = natural language processing components including vectorizers, models, sample datasets and renderers
• scaleout = integrations
  • spark = integration with Apache Spark versions 1.3 to 1.6 (Spark 2.0 coming soon)
  • parallel-wraper = Single machine model parallelism (for multi-GPU systems, etc)
  • aws = loading data to and from aws resources EC2 and S3
• ui = provides visual interfaces for tuning models Details here

Documentation is available at deeplearning4j.org and JavaDocs.

We are not supporting Stackoverflow right now. Github issues should focus on bug reports and feature requests. Please join the community on Gitter, where we field questions about how to install the software and work with neural nets. For support from Skymind, please see our contact page.

To install Deeplearning4J, there are a couple approaches briefly described on our Quickstart and below. More information can be found on the ND4J web site as well as here.

Search Maven Central for deeplearning4j to get a list of dependencies.

Add the dependency information to your pom.xml file. We highly recommend downloading via Maven unless you plan to help us develop DL4J.

1. Check for open issues or open a fresh one to start a discussion around a feature idea or a bug.
2. If you feel uncomfortable or uncertain about an issue or your changes, don't hesitate to contact us on Gitter using the link above.
3. Fork the repository on GitHub to start making your changes (branch off of the master branch).
4. Write a test that shows the bug was fixed or the feature works as expected.
5. Note the repository follows the Google Java style with two modifications: 120-char column wrap and 4-spaces indentation. You can format your code to this format by typing mvn formatter:format in the subproject you work on, by using the contrib/formatter.xml at the root of the repository to configure the Eclipse formatter, or by using the INtellij plugin.
6. Send a pull request and bug us on Gitter until it gets merged and published. :)