Brutex
/
cavis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
cavis/nd4j
History
Alexander Stoyakin f5068f3980 Added missing Java ops wrappers (#122) 
* Timeouts added

* Added some ops

* Ops added

* Fixed tests

* Minor fix

* Some fixes

* Digamma added

* Small fixes

* Timeouts added

* Added some ops

* Ops added

* Fixed tests

* Minor fix

* Some fixes

* Digamma added

* Small fixes

* Fused batch norm fixes-

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Tests switched off.

* Added test for resize_bicubic.

* Eliminated wasted in test of bicubic resize.

* Switched off multithreading explicit.

* HsvToRgb and RgbToHsv added

* Eliminated waste comments and conform proper float constants.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Fixed multithreading with resize_bicubic helper for cpu platform.

Signed-off-by: shugeo <sgazeos@gmail.com>

* ResizeBicubic was fixed.

* Some fixes

* Fix op name

* Validation fixed.

* Clarifications for tests

* Wrappers and small fixes for new ops.
 		2019-12-19 20:15:48 +11:00
..
ci Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
contrib Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
nd4j-backends Added missing Java ops wrappers (#122) 2019-12-19 20:15:48 +11:00
nd4j-buffer better handling of INDArray.close() (#154) 2019-08-23 10:24:56 +03:00
nd4j-common [WIP] Platform helpers switches (#44) 2019-11-14 14:35:02 +03:00
nd4j-context SameDiff cleanup and fixes (#12) 2019-10-26 12:38:08 +11:00
nd4j-jdbc Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
nd4j-parameter-server-parent Various fixes (#43) 2019-11-14 19:38:20 +11:00
nd4j-remote Add support for CUDA 10.2 (#89) 2019-11-29 16:31:03 +11:00
nd4j-serde J9+ -> J8 ByteBuffer fix (#59) 2019-11-20 07:43:17 +03:00
nd4j-shade nd4j-jackson: exclude java.xml.stream.XML*Factory from service loader to avoid clashes with other non-shaded jackson etc on classpath 2019-12-13 21:41:28 +11:00
nd4j-tensorflow TF Updates (#87) 2019-12-04 17:11:03 +11:00
nd4j-uberjar Add support for CUDA 10.2 (#89) 2019-11-29 16:31:03 +11:00
.appveyor.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.codeclimate.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.gitignore Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.travis.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
LICENSE Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
README.md Various fixes (#43) 2019-11-14 19:38:20 +11:00
RaspberryPi.md Update links to eclipse repos (#252) 2019-09-10 19:09:46 +10:00
VERSION Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
buildAllversions.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
buildmultiplescalaversions.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
pom.xml Version upgrades (#199) 2019-08-30 14:35:27 +10:00

README.md

ND4J: Scientific Computing on the JVM

Join the chat at https://gitter.im/deeplearning4j/deeplearning4j Maven Central Javadoc

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

  1. Search for nd4j in the Maven Central Repository to find the available nd4j jars.
  2. Include the appropriate dependency in your pom.xml.

Clone from the GitHub Repo

https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source

Contribute

  1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.

  2. If you feel uncomfortable or uncertain about an issue or your changes, feel free 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. 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.

  6. Send a pull request, and bug us on Gitter until it gets merged and published.