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* #8160 Remove resolvePrepertiesFromSameDiffBeforeExecution Signed-off-by: AlexDBlack <blacka101@gmail.com> * SameDiff API cleanup Signed-off-by: AlexDBlack <blacka101@gmail.com> * More SameDiff cleanup Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8248 Switch SameDiff variable init from lazy to creation time for more predictable behaviour Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8252 TanhDerivative javadoc Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8225 Deconvolution2D input validation Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8265 Switch SameDiff.outputs() to user settable, instead of unreliable 'best guess' Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8224 SameDiff.zero and .one create constants, not variables Signed-off-by: AlexDBlack <blacka101@gmail.com> * More cleanup and fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small test fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * DL4J SameDiff fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Re-add hack for Deconvolution2DLayer until #8315 is resolved Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8270 Move CUDA device/version logging to Java; can be disabled via existing org.nd4j.log.initialization system property Signed-off-by: AlexDBlack <blacka101@gmail.com> * All ND4J init logging checks system property Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small tweak Signed-off-by: AlexDBlack <blacka101@gmail.com> * Remove redundant device logging Signed-off-by: AlexDBlack <blacka101@gmail.com> * One more fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * UX improvements Signed-off-by: AlexDBlack <blacka101@gmail.com> * Deconv fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Add deconv tests Signed-off-by: AlexDBlack <blacka101@gmail.com> * Cleanup Signed-off-by: AlexDBlack <blacka101@gmail.com> * Remove debug code Signed-off-by: AlexDBlack <blacka101@gmail.com> |
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ci | ||
contrib | ||
nd4j-backends | ||
nd4j-buffer | ||
nd4j-common | ||
nd4j-context | ||
nd4j-jdbc | ||
nd4j-parameter-server-parent | ||
nd4j-remote | ||
nd4j-serde | ||
nd4j-shade | ||
nd4j-tensorflow | ||
nd4j-uberjar | ||
.appveyor.yml | ||
.codeclimate.yml | ||
.gitignore | ||
.travis.yml | ||
LICENSE | ||
README.md | ||
RaspberryPi.md | ||
VERSION | ||
buildAllversions.sh | ||
buildmultiplescalaversions.sh | ||
pom.xml |
README.md
ND4J: Scientific Computing on the JVM
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/eclipse/deeplearning4j-examples/blob/master/pom.xml
Main Features
- Versatile n-dimensional array object
- Multiplatform functionality including GPUs
- Linear algebra and signal processing functions
Specifics
- Supports GPUs via with the CUDA backend nd4j-cuda-7.5 and Native via nd4j-native.
- All of this is wrapped in a unifying interface.
- The API mimics the semantics of Numpy, Matlab and scikit-learn.
Documentation
Documentation is available at deeplearning4j.org. Access the JavaDocs for more detail.
Installation
To install ND4J, there are a couple of approaches, and more information can be found on the DL4J website.
Install from Maven Central
- Search for nd4j in the Maven Central Repository to find the available nd4j jars.
- Include the appropriate dependency in your pom.xml.
Clone from the GitHub Repo
https://deeplearning4j.org/buildinglocally
Contribute
-
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.
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Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
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Write a test, which shows that the bug was fixed or that the feature works as expected.
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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 thecontrib/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.