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MatMul for gemm/gemv calls (#365)
* libnd4j added optional alpha and beta support to matmul

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j typos fixes

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j add optional alpha and beta to matmul_bp

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j one more typo fix

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j added optional alpha and beta to mkl implementation

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* MatMul alpha/beta on java side

Signed-off-by: raver119 <raver119@gmail.com>

* alpha/beta fix in libnd4j

Signed-off-by: raver119 <raver119@gmail.com>

* alpha/beta fix in matmul_bp

Signed-off-by: raver119 <raver119@gmail.com>

* restored view validation

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* gemv/gemm now use MatMul op

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* few tests fixed

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* additional INDArray.mmul signature

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* make C order default for INDArray.mmul, unless both A/B have F order

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* Nd4j.gemm validation fix

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* disable mkldnn matmul for xxf with beta != 0 case

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* SimpleRnn workspace fix + timeouts

Signed-off-by: Alex Black <blacka101@gmail.com>

* two more tests + minor fix in matmul platform check

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* Flaky test fixes

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* propagate testresources profile

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* Resources fix + flaky test fix

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Co-authored-by: Oleg <oleg.semeniv@gmail.com>
Co-authored-by: Alex Black <blacka101@gmail.com>
2020-04-10 17:57:02 +03:00
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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:

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