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Fariz Rahman 2ecabde500
DL4J NWC support for RNNs (#379)
* merge conf

* merge conf

* conf fix

* NWC initial

* revert pom.xml

* revert pom.xml

* default NCW

* bidirectional+some tests

* RNNOutputLayer, RNNLossLayer, Graves + tests

* rnn tests

* LastTimeStep + tests

* masking + tests

* graves, rnnoutput, rnnloss

* nwc timeseries reverse

* more tests

* bi-gravelstm test

* fixes

* rnn df tests basic

* bug fix: cudnn fallback

* bug fix

* misc

* gravelstm tests

* preprocessor fixes

* TimeDistributed

* more tests

* RnnLossLayer builder def val

* copyright headers

* Remove debug println

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

* Small fix + test naming

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

* Parameterized test name

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

* fix LastTimeStep masked

* Fix MaskZero mask datatype issue

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

* rem println

* javadoc

* Fixes

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

Co-authored-by: Alex Black <blacka101@gmail.com>
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README.md

Monorepo of Deeplearning4j

Welcome to the new monorepo of Deeplearning4j that contains the source code for all the following projects, in addition to the original repository of Deeplearning4j moved to deeplearning4j:

To build everything, we can use commands like

./change-cuda-versions.sh x.x
./change-scala-versions.sh 2.xx
./change-spark-versions.sh x
mvn clean install -Dmaven.test.skip -Dlibnd4j.cuda=x.x -Dlibnd4j.compute=xx

or

mvn -B -V -U clean install -pl '!jumpy,!pydatavec,!pydl4j' -Dlibnd4j.platform=linux-x86_64 -Dlibnd4j.chip=cuda -Dlibnd4j.cuda=9.2 -Dlibnd4j.compute=<your GPU CC> -Djavacpp.platform=linux-x86_64 -Dmaven.test.skip=true

An example of GPU "CC" or compute capability is 61 for Titan X Pascal.

Want some examples?

We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples

In the examples repo, you'll also find a tutorial series in Zeppelin: https://github.com/eclipse/deeplearning4j-examples/tree/master/tutorials