4d81af9fe9
* libnd4j first step of softmax mkldnn implementation Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j raw implementation of mkldnn softmax Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j merge master and added softmax to MklDnnTests Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j some corrections for softmax mkldnn Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j merge branch, fixed problem with negative axis, fixed dnnl::memory::format_tag selection, test cases added Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j minor corrections to avoid risk connected with negative axis usage Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed windows builds, added switcher to use mkldnn sofmax version only for 3D, 4D, 5D, 6D arrays Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed dataType selection per request Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fix for mac and windows builds Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j builds fix Signed-off-by: Oleg <oleg.semeniv@gmail.com> |
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|---|---|---|
| .github | ||
| arbiter | ||
| datavec | ||
| deeplearning4j | ||
| docs | ||
| jumpy | ||
| libnd4j | ||
| nd4j | ||
| nd4s | ||
| pydatavec | ||
| pydl4j | ||
| rl4j | ||
| scalnet | ||
| .gitignore | ||
| CONTRIBUTING.md | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| change-cuda-versions.sh | ||
| change-scala-versions.sh | ||
| perform-release.sh | ||
| pom.xml | ||
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:
- 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