009007120b
* Implementation for non_max_suppression_v3 was added. Initial version * Added check for overcome threshold. * Added definition for V3 method. * java remapping for NonMaxSuppressionV3 Signed-off-by: raver119 <raver119@gmail.com> * Fixed proporly processing of an empty output and test. * Refactored op to less threshold data to float. * Implemented cuda-based helper for non_max_suppression_v3 op. * Fixed fake_quant_with_min_max_vars op. * Fixed tests with float numbers. * - assert now stops execution - sortByKey/sortByValue now have input validation Signed-off-by: raver119 <raver119@gmail.com> * missing var Signed-off-by: raver119 <raver119@gmail.com> * Fixed proper processing for zero max_size inputs. * Refactored kernel callers. * Fixed return statement for logdet op helper. * Refactored unsorted segment SqrtN op. * get back 8 tail bytes on CUDA Signed-off-by: raver119 <raver119@gmail.com> * Refactored segment prod ops and helpers for cuda and tests. * Additional test. * CudaWorkspace tests updated for 8 tail bytes Signed-off-by: raver119 <raver119@gmail.com> * special atomic test Signed-off-by: raver119 <raver119@gmail.com> * atomicMul/atomicDiv fix for 16bit values Signed-off-by: raver119 <raver119@gmail.com> * Eliminated waste prints. |
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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