f79207033b
* #8682 Don't log openmp BLAS threads for CUDA Signed-off-by: Alex Black <blacka101@gmail.com> * #8654 Add SameDiff multi-threaded tests Signed-off-by: Alex Black <blacka101@gmail.com> * Switching to op context for SameDiff exec Signed-off-by: Alex Black <blacka101@gmail.com> * Next steps Signed-off-by: Alex Black <blacka101@gmail.com> * Most back to passing Signed-off-by: Alex Black <blacka101@gmail.com> * Fixes Signed-off-by: Alex Black <blacka101@gmail.com> * Better tests, test refactoring Signed-off-by: Alex Black <blacka101@gmail.com> * Small tweak Signed-off-by: Alex Black <blacka101@gmail.com> * Code duplication reduction Signed-off-by: Alex Black <blacka101@gmail.com> * More code deduplication Signed-off-by: Alex Black <blacka101@gmail.com> * CUDA fixes Signed-off-by: Alex Black <blacka101@gmail.com> * More CUDA fixes Signed-off-by: Alex Black <blacka101@gmail.com> * More fixes Signed-off-by: Alex Black <blacka101@gmail.com> * Small fix Signed-off-by: Alex Black <blacka101@gmail.com> * ND4S small fixes Signed-off-by: Alex Black <blacka101@gmail.com> |
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| nd4j-tensorflow | ||
| nd4j-uberjar | ||
| .appveyor.yml | ||
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| .gitignore | ||
| .travis.yml | ||
| LICENSE | ||
| README.md | ||
| RaspberryPi.md | ||
| VERSION | ||
| buildAllversions.sh | ||
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| 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/docs/latest/deeplearning4j-build-from-source
Contribute
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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:formatin the subproject you work on, by using thecontrib/formatter.xmlat 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.