0748c7e7c2
* libnd4j raw implementation of native broadcast for special cases Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed bugs for special case of 4D loop broadcast, add some tests, need more testing and discussion Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j added 3D and 5D cases support and tests, need testing with different orders Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j correctd case selection for broadcast 3,4,5D loops, fixed several places for more stable behavior, clean up Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j minor corrections to avoid some risks in strides selection, added tests and rename some variables Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j optimize usage the stride selection for all loops in separate ShapeUtils method copyCertainStridesFromShapeInfo, merge master Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j remove per request several tests for 3D, 4D and 5D broadcast loops Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j removed some loac changes that had not been sync with serve playground, turn on new loops usage
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:
- https://github.com/eclipse/deeplearning4j/tree/master/libnd4j
- https://github.com/eclipse/deeplearning4j/tree/master/nd4j
- https://github.com/eclipse/deeplearning4j/tree/master/datavec
- https://github.com/eclipse/deeplearning4j/tree/master/arbiter
- https://github.com/eclipse/deeplearning4j/tree/master/nd4s
- https://github.com/eclipse/deeplearning4j/tree/master/rl4j
- https://github.com/eclipse/deeplearning4j/tree/master/scalnet
- https://github.com/eclipse/deeplearning4j/tree/master/pydl4j
- https://github.com/eclipse/deeplearning4j/tree/master/jumpy
- https://github.com/eclipse/deeplearning4j/tree/master/pydatavec
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