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Platform helpers (#8216) 
* platform helpers draft

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* typo

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* disable platform cmake

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* another draft

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* mkldnn convolution refactored

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* minor tweaks

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* one more safety check

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* prototype works

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* meh

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* force static library mode for mkldnn

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* - ismax fix
- experimental arg fix
- don't enforce openblas on Apple hardware

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* bunch of small fixes

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* declare concurrent

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* - MKLDNN version upgrade to 1.0.2
- avgpool2d/maxpool2d APIs update

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* - avgpool2d_bp/maxpool2d_bp APIs update

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* - conv2d/batchnorm APIs update

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* - lrn/conv2d_bp/conv3d/conv3d_bp APIs update

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* all ops converted to MKLDNN 1.x

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* bunch of tweaks

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* namespace for platform helpers

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* make sure platform helpers aren't opimized out

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* build cpu_features on x86 systems

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* build cpu_features on x86 systems

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* more of cpu_features

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* - mkldnn removed from java
- cpu_features checks in CpuNDArrayFactory

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* F16C definition renamed

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* some mkldnn rearrangements

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* check supported instructions before doing anything

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* typo

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* missied impl

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* BUILD_PIC option

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* conv2d fix

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* avgpool3d fix

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* avgpool3d_bp fix

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* avgpool2d_bp leak fix

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* avgpool3d_bp leak fix

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* maxpool bp leaks fixed

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* printf removed

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* batchnorm fix

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* AVX warning/error polishing

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* Fix

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* More polish

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* Polish

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* remove previous MKL-DNN support layer

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* avx2 tweak

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* allow static for apple

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* exclude mkldnn in one more place

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* exclude mkldnn in one more place

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* restore OPENBLAS_PATH use

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* add runtime check for avx/avx2 support

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* convolution_auto

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* Add logic for helper argument

* minor test fix

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* few tweaks

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* few tweaks

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* skip OpTracker props for non-x86 builds

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* linux arm isn't x86 :)

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* avx-512

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* CUDA presets fix

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* BUILD_PIC

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* prefetchw for avx2

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* BUILD_PIC again

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 		2019-09-11 21:50:28 +03:00
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.github Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
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 Platform helpers (#8216) 2019-09-11 21:50:28 +03:00
nd4j-buffer better handling of INDArray.close() (#154) 2019-08-23 10:24:56 +03:00
nd4j-common Platform helpers (#8216) 2019-09-11 21:50:28 +03:00
nd4j-context Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
nd4j-jdbc Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
nd4j-parameter-server-parent Version upgrades (#199) 2019-08-30 14:35:27 +10:00
nd4j-remote Fixes (#202) 2019-08-30 16:36:14 +10:00
nd4j-serde Version upgrades (#199) 2019-08-30 14:35:27 +10:00
nd4j-shade Version upgrades (#199) 2019-08-30 14:35:27 +10:00
nd4j-tensorflow Upgrade protobuf version (#162) 2019-08-24 19:22:36 +10:00
nd4j-uberjar Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.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 Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
RaspberryPi.md Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
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/deeplearning4j/dl4j-0.4-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/buildinglocally

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.