97 lines
6.2 KiB
Markdown
Executable File
97 lines
6.2 KiB
Markdown
Executable File
Eclipse Deeplearning4J: Neural Networks for Java/JVM
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=========================
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[](https://gitter.im/deeplearning4j/deeplearning4j?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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[](https://maven-badges.herokuapp.com/maven-central/org.deeplearning4j/deeplearning4j-core)
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[](https://deeplearning4j.org/api/latest/)
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Eclipse Deeplearning4J is part of the [Skymind Intelligence Layer](https://docs.skymind.ai/docs), along with ND4J, DataVec, Arbiter and RL4J. It 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.
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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.
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The most recent stable release in Maven Central is `0.9.1`, and the current master on Github can be built from source.
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For more info, see: https://docs.skymind.ai/docs
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---
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## Using Eclipse Deeplearning4j
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To get started using Deeplearning4j, please go to our [Quickstart](https://deeplearning4j.org/docs/latest/deeplearning4j-quickstart). You'll need to be familiar with a Java automated build tool such as Maven and an IDE such as IntelliJ.
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## Main Features
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- Versatile n-dimensional array class
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- GPU integration (supports devices starting from Kepler, cc3.0. You can check your device's compute compatibility [here](https://developer.nvidia.com/cuda-gpus).)
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---
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## Modules
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- datavec = Library for converting images, text and CSV data into format suitable for Deep Learning
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- nn = core neural net structures MultiLayer Network and Computation graph for designing Neural Net structures
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- core = additional functionality building on deeplearning4j-nn
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- modelimport = functionality to import models from Keras
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- nlp = natural language processing components including vectorizers, models, sample datasets and renderers
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- scaleout = integrations
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- spark = integration with Apache Spark versions 1.3 to 1.6 (Spark 2.0 coming soon)
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- parallel-wraper = Single machine model parallelism (for multi-GPU systems, etc)
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- aws = loading data to and from aws resources EC2 and S3
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- ui = provides visual interfaces for tuning models. [Details here](https://deeplearning4j.org/docs/latest/deeplearning4j-nn-visualization)
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---
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## Documentation
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Documentation is available at [deeplearning4j.org](https://deeplearning4j.org/overview) and [JavaDocs](https://deeplearning4j.org/api/latest/). Open-source contributors can help us improve our documentation for Deeplearning4j by sending pull requests for the DL4J website [here](https://github.com/eclipse/deeplearning4j-docs)
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## Support
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We are not supporting Stackoverflow right now. Github issues should focus on bug reports and feature requests. Please join the community on [Gitter](https://gitter.im/deeplearning4j/deeplearning4j), where we field questions about how to install the software and work with neural nets. For support from Skymind, please see our [contact page](https://skymind.io/contact).
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## Installation
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To install Deeplearning4J, see our [Quickstart](https://deeplearning4j.org/docs/latest/deeplearning4j-quickstart) and below. More information can be found on the [ND4J web site](http://nd4j.org/getstarted.html) as well as [here](https://deeplearning4j.org/tutorials/00-quickstart-for-deeplearning4j).
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#### Use Maven Central Repository
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Search Maven Central for [deeplearning4j](https://search.maven.org/#search%7Cga%7C1%7Cdeeplearning4j) to get a list of dependencies.
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Add the dependency information to your `pom.xml` file. **We highly recommend downloading via Maven unless you plan to help us develop DL4J.** An easy way to get up-to-date dependencies is to use the ones listed in our [dl4j-examples POM](https://github.com/eclipse/deeplearning4j-examples/blob/master/pom.xml).
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<!--
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#### Yum Install / Load RPM (Fedora or CentOS)
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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.
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$ sudo vi /etc/yum.repos.d/dl4j.repo
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Add the following to the `dl4j.repo` file:
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[dl4j.repo]
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name=dl4j-repo
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baseurl=http://ec2-52-5-255-24.compute-1.amazonaws.com/repo/RPMS
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enabled=1
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gpgcheck=0
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Then run the following command on the dl4j repo packages to install them on your machine:
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$ sudo yum install [package name] -y
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$ sudo yum install DL4J-Distro -y
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Note, be sure to install the ND4J modules you need first, especially the backend and then install DataVec and DL4J.
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-->
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---
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## Contribute
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1. Check for [open issues](https://github.com/eclipse/deeplearning4j/issues) or open a fresh one to start a discussion around a feature idea or a bug.
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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.
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3. Fork [the repository](https://github.com/eclipse/deeplearning4j.git)
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on GitHub to start making your changes (branch off of the master branch).
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4. Write a test that shows the bug was fixed or the feature works as expected.
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5. Note the repository follows
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the [Google Java style](https://google.github.io/styleguide/javaguide.html)
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with two modifications: 120-char column wrap and 4-spaces indentation. You
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can format your code to this format by typing `mvn formatter:format` in the
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subproject you work on, by using the `contrib/formatter.xml` at the root of
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the repository to configure the Eclipse formatter, or by [using the Intellij
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plugin](https://github.com/HPI-Information-Systems/Metanome/wiki/Installing-the-google-styleguide-settings-in-intellij-and-eclipse).
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6. Send a pull request and bug us on Gitter until it gets merged and published. :)
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7. Add technical documentation on the [Deeplearning4j website](https://github.com/eclipse/deeplearning4j/tree/gh-pages) and fix any typos you see.
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