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* #8160 Remove resolvePrepertiesFromSameDiffBeforeExecution Signed-off-by: AlexDBlack <blacka101@gmail.com> * SameDiff API cleanup Signed-off-by: AlexDBlack <blacka101@gmail.com> * More SameDiff cleanup Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8248 Switch SameDiff variable init from lazy to creation time for more predictable behaviour Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8252 TanhDerivative javadoc Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8225 Deconvolution2D input validation Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8265 Switch SameDiff.outputs() to user settable, instead of unreliable 'best guess' Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8224 SameDiff.zero and .one create constants, not variables Signed-off-by: AlexDBlack <blacka101@gmail.com> * More cleanup and fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small test fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * DL4J SameDiff fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Re-add hack for Deconvolution2DLayer until #8315 is resolved Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8270 Move CUDA device/version logging to Java; can be disabled via existing org.nd4j.log.initialization system property Signed-off-by: AlexDBlack <blacka101@gmail.com> * All ND4J init logging checks system property Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small tweak Signed-off-by: AlexDBlack <blacka101@gmail.com> * Remove redundant device logging Signed-off-by: AlexDBlack <blacka101@gmail.com> * One more fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * UX improvements Signed-off-by: AlexDBlack <blacka101@gmail.com> * Deconv fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Add deconv tests Signed-off-by: AlexDBlack <blacka101@gmail.com> * Cleanup Signed-off-by: AlexDBlack <blacka101@gmail.com> * Remove debug code Signed-off-by: AlexDBlack <blacka101@gmail.com> |
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| .github | ||
| arbiter | ||
| datavec | ||
| deeplearning4j | ||
| docs | ||
| gym-java-client | ||
| 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/gym-java-client
- 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