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Alex Black 88d3c4867f
Refactor packages to fix split package issues (#411) 
* Refactor nd4j-common: org.nd4j.* -> org.nd4j.common.*

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

* Fix CUDA (missed nd4j-common package refactoring changes)

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

* nd4j-kryo: org.nd4j -> org.nd4j.kryo

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

* Fix nd4j-common for deeplearning4j-cuda

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

* nd4j-grppc-client: org.nd4j.graph -> org.nd4j.remote.grpc

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

* deeplearning4j-common: org.deeplearning4.* -> org.deeplearning4j.common.*

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

* deeplearning4j-core: org.deeplearning4j.* -> org.deeplearning.core.*

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

* deeplearning4j-cuda: org.deeplearning4j.nn.layers.* -> org.deeplearning4j.cuda.*

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

* Import fixes

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

* deeplearning4j-nlp-*: org.deeplearning4.text.* -> org.deeplearning4j.nlp.(language).*

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

* deeplearning4j-ui-model: org.deeplearning4j.ui -> org.deeplearning4j.ui.model

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

* datavec-spark-inference-{server/model/client}: org.datavec.spark.transform -> org.datavec.spark.inference.{server/model/client}

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

* datavec-jdbc: org.datavec.api -> org.datavec.jdbc

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

* Delete org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter in favor of (essentially identical) org.nd4j.linalg.dataset.adapter.MultiDataSetIteratorAdapter

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

* ND4S fixes

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

* Fixes

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

* nd4j-common-tests: org.nd4j.* -> org.nd4j.common.tests

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

* Trigger CI

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

* Fixes

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

* #8878 Ignore CUDA tests on modules with 'nd4j-native under cuda' issue

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

* Fix bad imports in tests

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

* Add ignore on test (already failing) due to #8882

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

* Import fixes

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

* Additional import fixes

Signed-off-by: Alex Black <blacka101@gmail.com>
 		2020-04-29 11:19:26 +10:00
..
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 Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
nd4j-common Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
nd4j-common-tests Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
nd4j-jdbc Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
nd4j-parameter-server-parent Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
nd4j-remote Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
nd4j-serde Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
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 Refactor packages to fix split package issues (#411) 2020-04-29 11:19:26 +10:00
nd4j-uberjar Fix uberjar dependencies (#401) 2020-04-21 10:51:26 +10:00
.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 Cleanup of multiple projects (#175) 2020-01-24 22:35:00 +03:00

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.