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Platform helpers (#8216)
* platform helpers draft

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* typo

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* disable platform cmake

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* another draft

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* mkldnn convolution refactored

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* minor tweaks

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* one more safety check

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* prototype works

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* meh

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* force static library mode for mkldnn

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* - ismax fix
- experimental arg fix
- don't enforce openblas on Apple hardware

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* bunch of small fixes

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* declare concurrent

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* - MKLDNN version upgrade to 1.0.2
- avgpool2d/maxpool2d APIs update

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* - avgpool2d_bp/maxpool2d_bp APIs update

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* - conv2d/batchnorm APIs update

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* - lrn/conv2d_bp/conv3d/conv3d_bp APIs update

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* all ops converted to MKLDNN 1.x

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* bunch of tweaks

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* namespace for platform helpers

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* make sure platform helpers aren't opimized out

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* build cpu_features on x86 systems

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* build cpu_features on x86 systems

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* more of cpu_features

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* - mkldnn removed from java
- cpu_features checks in CpuNDArrayFactory

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* F16C definition renamed

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* some mkldnn rearrangements

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* check supported instructions before doing anything

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* typo

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* missied impl

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* BUILD_PIC option

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* conv2d fix

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* avgpool3d fix

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* avgpool3d_bp fix

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* avgpool2d_bp leak fix

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* avgpool3d_bp leak fix

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* maxpool bp leaks fixed

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* printf removed

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* batchnorm fix

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* AVX warning/error polishing

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* Fix

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* More polish

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* Polish

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* remove previous MKL-DNN support layer

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* avx2 tweak

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* allow static for apple

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* exclude mkldnn in one more place

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* exclude mkldnn in one more place

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* restore OPENBLAS_PATH use

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* add runtime check for avx/avx2 support

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* convolution_auto

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* Add logic for helper argument

* minor test fix

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* few tweaks

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* few tweaks

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* skip OpTracker props for non-x86 builds

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* linux arm isn't x86 :)

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* avx-512

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* CUDA presets fix

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* BUILD_PIC

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* prefetchw for avx2

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* BUILD_PIC again

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2019-09-11 21:50:28 +03:00
.github Update contributing and issue/PR templates (#7934) 2019-06-22 16:21:27 +10:00
arbiter Small Arbiter UI fix (#244) 2019-09-05 19:05:40 +10:00
datavec Small DataVec test fix (#239) 2019-09-05 12:26:54 +10:00
deeplearning4j Version fix (#245) 2019-09-06 12:06:17 +10:00
docs Dl4j LSTM and Dropout CuDNN fallback and options (#152) 2019-08-29 13:05:01 +10:00
gym-java-client Fix backend dependencies for tests (#189) 2019-08-29 12:54:48 +09:00
jumpy Fix backend dependencies for tests (#189) 2019-08-29 12:54:48 +09:00
libnd4j Platform helpers (#8216) 2019-09-11 21:50:28 +03:00
nd4j Platform helpers (#8216) 2019-09-11 21:50:28 +03:00
nd4s ND4S test fix (#210) 2019-08-31 12:27:09 +10:00
pydatavec Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
pydl4j Fix backend dependencies for tests (#189) 2019-08-29 12:54:48 +09:00
rl4j Version upgrades (#199) 2019-08-30 14:35:27 +10:00
scalnet Version upgrades (#199) 2019-08-30 14:35:27 +10:00
.gitignore Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
CONTRIBUTING.md Update contributing and issue/PR templates (#7934) 2019-06-22 16:21:27 +10:00
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README.md Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
change-cuda-versions.sh Update dependencies to just released JavaCPP and JavaCV 1.5.1 (#8004) 2019-07-14 21:07:33 +03:00
change-scala-versions.sh Version upgrades (#199) 2019-08-30 14:35:27 +10:00
perform-release.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
pom.xml Update dependency on JavaCPP to just released version 1.5.1-1 2019-09-05 22:10:39 +09:00

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/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