Fast, Scientific and Numerical Computing for the JVM (NDArrays)
ND4J is an Apache 2.0-licensed scientific computing library for the JVM. By contributing code to this repository, you agree to make your contribution available under an Apache 2.0 license.
It is meant to be used in production environments rather than as a research tool, which means routines are designed to run fast with minimum RAM requirements.
Please search for the latest version on search.maven.org.
Or use the versions displayed in: https://github.com/deeplearning4j/dl4j-0.4-examples/blob/master/pom.xml
Specifics
Several of these modules are different backend options for ND4J (including GPUs).
It is possible to build the project without the native bindings. This can be done by specic targeting of the project to build.
mvn clean package test -pl :nd4j-api
Documentation is available at nd4j.org. Access the JavaDocs for more detail.
To install ND4J, there are a couple of approaches, and more information can be found on the ND4J website.
https://deeplearning4j.org/buildinglocally
Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
Write a test, which shows that the bug was fixed or that the feature works as expected.
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.
Send a pull request, and bug us on Gitter until it gets merged and published.