e700b59f80
* - start to introduce additional weights formats into conv2d ops Signed-off-by: Yurii <iuriish@yahoo.com> * - provide weights format variety in backprop conv2d and deconv2d ops, testing and fixing bugs Signed-off-by: Yurii <iuriish@yahoo.com> * - forgot to recover kernels sizes in deconv2d_bp test Signed-off-by: Yurii <iuriish@yahoo.com> * - built in weights format in depthwise conv 2d op Signed-off-by: Yurii <iuriish@yahoo.com> * - provide new weights formats in mkl dnn conv ops Signed-off-by: Yurii <iuriish@yahoo.com> * - provide new weights formats in cuda conv helpers Signed-off-by: Yurii <iuriish@yahoo.com> * - working with new weights format in cudnn conv api Signed-off-by: Yurii <iuriish@yahoo.com> * - take into account order of arrays in cudnn tensor descriptions Signed-off-by: Yurii <iuriish@yahoo.com> * - provide new weights formats in cpu conv3d (ff/bp) Signed-off-by: Yurii <iuriish@yahoo.com> * - provide new weights formats in cpu deconv3d (ff/bp) Signed-off-by: Yurii <iuriish@yahoo.com> * - provide new weights formats in conv3d ops (ff/bp) based on mkl api Signed-off-by: Yurii <iuriish@yahoo.com> * - provide new weights formats in conv3d ops (ff/bp) based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - resolve conflicts 2 Signed-off-by: Yurii <iuriish@yahoo.com> Co-authored-by: raver119 <raver119@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