69d91e272a
* - new implementations for Index Reductions - small fix in the legacy reduction - disabled index reduction bench tests inside Playground Signed-off-by: Abdelrauf <rauf@konduit.ai> * Allow LIBND4J_TYPES Signed-off-by: Abdelrauf <rauf@konduit.ai> * index reduction stuff split into bunch of units * meh * IMax switched to new impl Signed-off-by: raver119@gmail.com <raver119@gmail.com> * minor fix + test * minor fix * index range fix Signed-off-by: Abdelrauf <rauf@konduit.ai> * noop on empty outputs * minor fix * minor fix Signed-off-by: Abdelrauf <rauf@konduit.ai> * ArgMax replaces IMax Signed-off-by: raver119@gmail.com <raver119@gmail.com> * argmax/argmin/argamax/argamin shape functions updated * ArgAmax/ArgAmin/ArgMin replaces IAMax/IAMin/IMin Signed-off-by: raver119@gmail.com <raver119@gmail.com> * argmax/argmin/argamax/argamin CUDA * IMax replaced in dl4j Signed-off-by: raver119@gmail.com <raver119@gmail.com> * Codegen output * imports fixed Signed-off-by: raver119@gmail.com <raver119@gmail.com> * fix compilation issue Signed-off-by: Abdelrauf <rauf@konduit.ai> * Auto-generate compilation units Signed-off-by: Abdelrauf <rauf@konduit.ai> * Should fix NDArray refactored function calls in indexReductions.cu Signed-off-by: Abdelrauf <rauf@konduit.ai> Co-authored-by: raver119@gmail.com <raver119@gmail.com> Co-authored-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
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