Deeplearning4J: Neural Net Platform

Deeplearning4J is an Apache 2.0-licensed, open-source, distributed neural net library written in Java and Scala.

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.

Current version in maven central is 0.4-rc0.

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Main Features

• Versatile n-dimensional array class
• GPU integration
• Scalable on Hadoop, Spark and Akka + AWS et al

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Modules

• cli = command line interface for deeplearning4j
• core = core neural net structures and supporting components such as datasets, iterators, clustering algorithms, optimization methods, evaluation tools and plots.
• scaleout = integrations
  • aws = loading data to and from aws resources EC2 and S3
  • nlp = natural language processing components including vecotrizers, models, sample datasets and renders
  • akka = setup concurrent and distributed applications on the JVM
  • api = core components like workers and multi-threading
  • zookeeper = maintain configuration for distributed systems
  • hadoop-yarn = common map-reduce distributed system
  • spark = integration with spark
    • dl4j-spark = spark 1.2-compatible
    • dl4j-spark-ml = spark 1.4-compatible, based on ML pipeline
• ui = provides visual interfaces with models like nearest neighbors
• test-resources = datasets and supporting components for tests

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Documentation

Documentation is available at deeplearning4j.org and JavaDocs.

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Installation

To install Deeplearning4J, there are a couple approaches (briefly described below). More information can be found on the ND4J website and here.

Use Maven Central Repository

Search for deeplearning4j to get a list of jars you can use.

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

Clone from the GitHub Repo

Deeplearning4J is being actively developed and you can clone the repository, compile it and reference it in your project.

For users who have never git cloned Deeplearning4j before, you should be able to install the framework, along with ND4J and Canova, by entering one line in your command prompt:

git clone https://github.com/deeplearning4j/deeplearning4j/; cd deeplearning4j;./setup.sh

This command will also get you ND4J and Canova, which you need.

To get the most recent updates from Github after an initial clone, pull the ND4J, Canova and DL4J repositories:

$ git pull git://github.com/deeplearning4j/nd4j.git
$ git pull git://github.com/deeplearning4j/Canova.git
$ git pull git://github.com/deeplearning4j/deeplearning4j.git

Compile the project from within each of the three directories, starting with ND4J:

$ cd deeplearning4j && mvn clean install -DskipTests -Dmaven.javadoc.skip=true

Add the local compiled file dependencies to your pom.xml file. Here's an example of what they'll look like:

<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-cli</artifactId>
    <version>0.4-rc0</version>
</dependency>

Yum Install / Load RPM (Fedora or CentOS)

Create a yum repo and run yum install to load the Red Hat Package Management (RPM) files. First create the repo file to setup the configuration locally.

$ sudo vi /etc/yum.repos.d/dl4j.repo 

Add the following to the dl4j.repo file:

'''

[dl4j.repo]

name=dl4j-repo
baseurl=http://ec2-52-5-255-24.compute-1.amazonaws.com/repo/RPMS
enabled=1
gpgcheck=0

'''

Then run the following command on the dl4j repo packages to install them on your machine:

$ sudo yum install [package name] -y
$ sudo yum install DL4J-Distro -y 

Note, be sure to install the nd4j modules you need first, especially the backend and then install Canova and dl4j.

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Contribute

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 to the master branch (or branch off of it).
4. Write a test which shows that the bug was fixed or that the feature works as expected.
5. Send a pull request and bug us on Gitter until it gets merged and published. :)