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* Upgrade presets for cuDNN 8.0.3
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‎CHANGELOG.md‎

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* Add support for Windows to presets for Qt ([issue #862](https://github.com/bytedeco/javacpp-presets/issues/862))
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* Fix JPMS modules for CUDA, ARPACK-NG, GSL, SciPy, Gym, MXNet ([pull #880](https://github.com/bytedeco/javacpp-presets/pull/880) and [pull #881](https://github.com/bytedeco/javacpp-presets/pull/881)), OpenCV, CPython, LLVM, Tesseract, Qt ([pull #928](https://github.com/bytedeco/javacpp-presets/pull/928))
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* Build OpenBLAS with a `TARGET` even for `DYNAMIC_ARCH` to avoid SIGILL ([issue eclipse/deeplearning4j#8747](https://github.com/eclipse/deeplearning4j/issues/8747))
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* Upgrade presets for OpenCV 4.4.0, FFmpeg 4.3.1 ([pull #891](https://github.com/bytedeco/javacpp-presets/pull/891)), Arrow 1.0.1, Hyperscan 5.3.0, MKL 2020.2, MKL-DNN 0.21.5, DNNL 1.6.1, OpenBLAS 0.3.10, CPython 3.7.9, NumPy 1.19.1, SciPy 1.5.2, Gym 0.17.2, LLVM 10.0.1, Leptonica 1.80.0, CUDA 11.0.3, cuDNN 8.0.2, NCCL 2.7.8, MXNet 1.7.0, TensorFlow 1.15.3, TensorRT 7.1, ONNX 1.7.0 ([pull #882](https://github.com/bytedeco/javacpp-presets/pull/882)), ONNX Runtime 1.4.0 ([pull #887](https://github.com/bytedeco/javacpp-presets/pull/887)), Qt 5.15.0, Skia 2.80.1, and their dependencies
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* Upgrade presets for OpenCV 4.4.0, FFmpeg 4.3.1 ([pull #891](https://github.com/bytedeco/javacpp-presets/pull/891)), Arrow 1.0.1, Hyperscan 5.3.0, MKL 2020.2, MKL-DNN 0.21.5, DNNL 1.6.1, OpenBLAS 0.3.10, CPython 3.7.9, NumPy 1.19.1, SciPy 1.5.2, Gym 0.17.2, LLVM 10.0.1, Leptonica 1.80.0, CUDA 11.0.3, cuDNN 8.0.3, NCCL 2.7.8, MXNet 1.7.0, TensorFlow 1.15.3, TensorRT 7.1, ONNX 1.7.0 ([pull #882](https://github.com/bytedeco/javacpp-presets/pull/882)), ONNX Runtime 1.4.0 ([pull #887](https://github.com/bytedeco/javacpp-presets/pull/887)), Qt 5.15.0, Skia 2.80.1, and their dependencies
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* Add `FullOptimization.h` allowing users to fully optimize LLVM modules ([pull #869](https://github.com/bytedeco/javacpp-presets/pull/869))
2020

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### April 14, 2020 version 1.5.3

‎ci/install-ppc.sh‎

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@@ -72,7 +72,7 @@ if [[ "$PROJ" =~ cuda ]]; then
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echo "Setting up for cuda build"
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cd $HOME/
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curl -L https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_ppc64el.deb -o $HOME/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_ppc64el.deb
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curl -L https://developer.download.nvidia.com/compute/redist/cudnn/v8.0.2/cudnn-11.0-linux-ppc64le-v8.0.2.39.tgz -o $HOME/cudnn-11.0-linux-ppc64le-v8.0.2.39.tgz
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curl -L https://developer.download.nvidia.com/compute/redist/cudnn/v8.0.3/cudnn-11.0-linux-ppc64le-v8.0.3.33.tgz -o $HOME/cudnn-11.0-linux-ppc64le-v8.0.3.33.tgz
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curl -L https://developer.download.nvidia.com/compute/redist/nccl/v2.7/nccl_2.7.8-1+cuda11.0_ppc64le.txz -o $HOME/nccl_ppc64le.txz
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ar vx $HOME/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_ppc64el.deb
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tar xvf data.tar.xz
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "ln -sf /usr/local/cuda-11.0 /usr/local/cuda"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "ln -sf /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/libcuda.so"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "ln -sf /usr/local/cuda/lib64/stubs/libnvidia-ml.so /usr/local/cuda/lib64/libnvidia-ml.so"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "tar hxvf $HOME/cudnn-11.0-linux-ppc64le-v8.0.2.39.tgz -C /usr/local/"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "tar hxvf $HOME/cudnn-11.0-linux-ppc64le-v8.0.3.33.tgz -C /usr/local/"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "tar hxvf $HOME/nccl_ppc64le.txz --strip-components=1 -C /usr/local/cuda/"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "mv /usr/local/cuda/lib/* /usr/local/cuda/lib64/"
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# work around issues with CUDA 10.2/11.0

‎ci/install-travis.sh‎

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@@ -158,14 +158,14 @@ if [[ "$OS" == "linux-x86" ]] || [[ "$OS" == "linux-x86_64" ]] || [[ "$OS" =~ an
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if [[ "$PROJ" =~ cuda ]] || [[ "$PROJ" == "tensorrt" ]] || [[ "$EXT" =~ gpu ]]; then
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echo "installing cuda, cudnn, and nccl.."
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curl -L https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda-repo-rhel7-11-0-local-11.0.3_450.51.06-1.x86_64.rpm -o $HOME/cuda-repo-rhel7-11-0-local-11.0.3_450.51.06-1.x86_64.rpm
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curl -L https://developer.download.nvidia.com/compute/redist/cudnn/v8.0.2/cudnn-11.0-linux-x64-v8.0.2.39.tgz -o $HOME/cudnn-11.0-linux-x64-v8.0.2.39.tgz
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curl -L https://developer.download.nvidia.com/compute/redist/cudnn/v8.0.3/cudnn-11.0-linux-x64-v8.0.3.33.tgz -o $HOME/cudnn-11.0-linux-x64-v8.0.3.33.tgz
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curl -L https://developer.download.nvidia.com/compute/redist/nccl/v2.7/nccl_2.7.8-1+cuda11.0_x86_64.txz -o $HOME/nccl_x86_64.txz
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "rpm -i $HOME/cuda-repo-rhel7-11-0-local-11.0.3_450.51.06-1.x86_64.rpm"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "cd /var/cuda-repo-rhel7-11-0-local/; rpm -i --nodeps cuda*.rpm libc*.rpm libn*.rpm"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "ln -sf /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/libcuda.so"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "ln -sf /usr/local/cuda/lib64/stubs/libnvidia-ml.so /usr/local/cuda/lib64/libnvidia-ml.so"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "tar hxvf $HOME/cudnn-11.0-linux-x64-v8.0.2.39.tgz -C /usr/local/"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "tar hxvf $HOME/cudnn-11.0-linux-x64-v8.0.3.33.tgz -C /usr/local/"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "tar hxvf $HOME/nccl_x86_64.txz --strip-components=1 -C /usr/local/cuda/"
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docker exec -ti $DOCKER_CONTAINER_ID /bin/bash -xec "mv /usr/local/cuda/lib/* /usr/local/cuda/lib64/"
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# work around issues with CUDA 10.2/11.0

‎ci/install-windows.sh‎

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if [ "$PROJ" == "cuda" ] || [ "$PROJ" == "tensorrt" ] || [ "$EXT" == "-gpu" ]; then
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echo Installing cuda
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curl -L -o cuda_11.0.3_451.82_win10.exe "https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda_11.0.3_451.82_win10.exe"
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curl -L -o cudnn-11.0-windows-x64-v8.0.2.39.zip "https://developer.download.nvidia.com/compute/redist/cudnn/v8.0.2/cudnn-11.0-windows-x64-v8.0.2.39.zip"
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curl -L -o cudnn-11.0-windows-x64-v8.0.3.33.zip "https://developer.download.nvidia.com/compute/redist/cudnn/v8.0.3/cudnn-11.0-windows-x64-v8.0.3.33.zip"
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./cuda_11.0.3_451.82_win10.exe -s
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sleep 60
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unzip ./cudnn-11.0-windows-x64-v8.0.2.39.zip
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unzip ./cudnn-11.0-windows-x64-v8.0.3.33.zip
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mv ./cuda/bin/*.dll /c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.0/bin
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mv ./cuda/include/*.h /c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.0/include
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mv ./cuda/lib/x64/*.lib /c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.0/lib/x64

‎cuda/README.md‎

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2020
This directory contains the JavaCPP Presets module for:
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* CUDA 11.0.3 https://developer.nvidia.com/cuda-zone
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* cuDNN 8.0.2 https://developer.nvidia.com/cudnn
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* cuDNN 8.0.3 https://developer.nvidia.com/cudnn
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* NCCL 2.7.8 https://developer.nvidia.com/nccl
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Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.

‎cuda/src/gen/java/org/bytedeco/cuda/global/cudnn.java‎

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public static final int CUDNN_MAJOR = 8;
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public static final int CUDNN_MINOR = 0;
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public static final int CUDNN_PATCHLEVEL = 2;
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public static final int CUDNN_PATCHLEVEL = 3;
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public static final int CUDNN_VERSION = (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL);
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/* These version numbers are autogenerated, do not edit manually. */
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public static final int CUDNN_OPS_INFER_MAJOR = 8;
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public static final int CUDNN_OPS_INFER_MINOR = 0;
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public static final int CUDNN_OPS_INFER_PATCH = 2;
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public static final int CUDNN_OPS_INFER_PATCH = 3;
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// #if (CUDNN_OPS_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_INFER_MINOR != CUDNN_MINOR) ||
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// (CUDNN_OPS_INFER_PATCH != CUDNN_PATCHLEVEL)
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/* These version numbers are autogenerated, do not edit manually. */
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public static final int CUDNN_OPS_TRAIN_MAJOR = 8;
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public static final int CUDNN_OPS_TRAIN_MINOR = 0;
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public static final int CUDNN_OPS_TRAIN_PATCH = 2;
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public static final int CUDNN_OPS_TRAIN_PATCH = 3;
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// #if (CUDNN_OPS_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_TRAIN_MINOR != CUDNN_MINOR) ||
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// (CUDNN_OPS_TRAIN_PATCH != CUDNN_PATCHLEVEL)
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/* These version numbers are autogenerated, do not edit manually. */
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public static final int CUDNN_ADV_INFER_MAJOR = 8;
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public static final int CUDNN_ADV_INFER_MINOR = 0;
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public static final int CUDNN_ADV_INFER_PATCH = 2;
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public static final int CUDNN_ADV_INFER_PATCH = 3;
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// #if (CUDNN_ADV_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_INFER_MINOR != CUDNN_MINOR) ||
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// (CUDNN_ADV_INFER_PATCH != CUDNN_PATCHLEVEL)
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// #include <stdint.h>
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// #include "cudnn_version.h"
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// #include "cudnn_adv_infer.h"
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// #include "cudnn_ops_infer.h"
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// #include "cudnn_ops_train.h"
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// #include "cudnn_adv_infer.h"
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/* These version numbers are autogenerated, do not edit manually. */
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public static final int CUDNN_ADV_TRAIN_MAJOR = 8;
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public static final int CUDNN_ADV_TRAIN_MINOR = 0;
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public static final int CUDNN_ADV_TRAIN_PATCH = 2;
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public static final int CUDNN_ADV_TRAIN_PATCH = 3;
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// #if (CUDNN_ADV_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_TRAIN_MINOR != CUDNN_MINOR) ||
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// (CUDNN_ADV_TRAIN_PATCH != CUDNN_PATCHLEVEL)
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/* These version numbers are autogenerated, do not edit manually. */
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public static final int CUDNN_CNN_INFER_MAJOR = 8;
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public static final int CUDNN_CNN_INFER_MINOR = 0;
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public static final int CUDNN_CNN_INFER_PATCH = 2;
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public static final int CUDNN_CNN_INFER_PATCH = 3;
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// #if (CUDNN_CNN_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_INFER_MINOR != CUDNN_MINOR) ||
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// (CUDNN_CNN_INFER_PATCH != CUDNN_PATCHLEVEL)
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// #include "cudnn_version.h"
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// #include "cudnn_ops_infer.h"
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// #include "cudnn_ops_train.h"
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// #include "cudnn_cnn_infer.h"
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/* These version numbers are autogenerated, do not edit manually. */
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public static final int CUDNN_CNN_TRAIN_MAJOR = 8;
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public static final int CUDNN_CNN_TRAIN_MINOR = 0;
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public static final int CUDNN_CNN_TRAIN_PATCH = 2;
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public static final int CUDNN_CNN_TRAIN_PATCH = 3;
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// #if (CUDNN_CNN_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_TRAIN_MINOR != CUDNN_MINOR) ||
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// (CUDNN_CNN_TRAIN_PATCH != CUDNN_PATCHLEVEL)

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