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* remove some unneeded java-side output shape calculations Signed-off-by: Ryan Nett <rnett@skymind.io> * delete Broadcast Signed-off-by: Ryan Nett <rnett@skymind.io> * delete Linear and Module, Signed-off-by: Ryan Nett <rnett@skymind.io> * update Identity, HashCode, and NoOp Signed-off-by: Ryan Nett <rnett@skymind.io> * removed Cast java-side shape function, added tests and SDVariable.isEmpty Signed-off-by: Ryan Nett <rnett@skymind.io> * ignoring test w/ issues on master Signed-off-by: Ryan Nett <rnett@skymind.io> * noop needs more work, fixed BaseArithmeticBackprop and BaseDynamicTransform ops merge in master for c++ build fix Signed-off-by: Ryan Nett <rnett@skymind.io> * fix EqualTo Signed-off-by: Ryan Nett <rnett@skymind.io> * fix other cond ops Signed-off-by: Ryan Nett <rnett@skymind.io> * "fake" ops calculateOutputShape() throws exception Signed-off-by: Ryan Nett <rnett@skymind.io> * use c++ shape calc for Linspace Signed-off-by: Ryan Nett <rnett@skymind.io> * fix exception message, move most to BaseCompatOp Signed-off-by: Ryan Nett <rnett@skymind.io> * remove SDVariable.isEmpty Signed-off-by: Ryan Nett <rnett@skymind.io> * remove commented out code Signed-off-by: Ryan Nett <rnett@skymind.io> |
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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 | ||
| change-spark-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/deeplearning4j/libnd4j
- https://github.com/deeplearning4j/nd4j
- https://github.com/deeplearning4j/datavec
- https://github.com/deeplearning4j/arbiter
- https://github.com/deeplearning4j/nd4s
- https://github.com/deeplearning4j/gym-java-client
- https://github.com/deeplearning4j/rl4j
- https://github.com/deeplearning4j/scalnet
- https://github.com/deeplearning4j/pydl4j
- https://github.com/deeplearning4j/jumpy
- https://github.com/deeplearning4j/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/deeplearning4j/dl4j-examples
In the examples repo, you'll also find a tutorial series in Zeppelin: https://github.com/deeplearning4j/dl4j-examples/tree/master/tutorials