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* Upgrade presets for DNNL 3.11, ONNX Runtime 1.24.1
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.github/actions/deploy-ubuntu/action.yml

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if [[ -n "$SUDO" ]]; then
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# Make sure no busy /swapfile is hanging around
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# First disable any existing swapfile with swapoff
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$SUDO swapoff /swapfile
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$SUDO rm -f /swapfile
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$SUDO swapoff /swapfile || true
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$SUDO rm -f /swapfile || true
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# Allocate a swapfile on Linux as it's not enabled by default. Needed for pytorch and mkl.
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$SUDO fallocate -l 16GB /swapfile

.github/actions/deploy-windows/action.yml

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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-readline-8.2.013-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-tcl-8.6.13-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-tk-8.6.13-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-crt-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-headers-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-libmangle-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-libwinpthread-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-tools-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-winpthreads-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-winstorecompat-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-crt-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-headers-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-libmangle-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-libwinpthread-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-tools-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-winpthreads-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-winstorecompat-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-vulkan-headers-1.3.296.0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-vulkan-loader-1.3.296.0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/i686/mingw-w64-i686-SDL2-2.30.12-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-readline-8.2.013-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-tcl-8.6.13-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-tk-8.6.13-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-crt-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-headers-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-libmangle-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-libwinpthread-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-tools-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-winpthreads-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-winstorecompat-git-11.0.0.r750.g05598db99-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-crt-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-headers-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-libmangle-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-libwinpthread-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-tools-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-winpthreads-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-winstorecompat-git-12.0.0.r1.gdc42231f0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-vulkan-headers-1.3.296.0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-vulkan-loader-1.3.296.0-1-any.pkg.tar.zst
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curl -LO http://repo.msys2.org/mingw/x86_64/mingw-w64-x86_64-SDL2-2.30.12-1-any.pkg.tar.zst

CHANGELOG.md

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* Introduce CUDA-enabled `linux-arm64-gpu` builds to presets for OpenCV ([pull #1651](https://github.com/bytedeco/javacpp-presets/pull/1651))
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* Reenable again `linux-arm64` builds for CPython and NumPy ([pull #1652](https://github.com/bytedeco/javacpp-presets/pull/1652))
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* Bundle libraries for VA-API 2.x with FFmpeg to avoid loading issues ([issue bytedeco/javacv#2340](https://github.com/bytedeco/javacv/issues/2340))
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* Upgrade presets for OpenCV 4.13.0, FFmpeg 8.0.1, MKL 2025.3.1, DNNL 3.10.2, OpenBLAS 0.3.31, CUDA 13.1.1, cuDNN 9.19.0.56, NCCL 2.29.3, nvCOMP 5.1.0.21, NVIDIA Video Codec SDK 13.0.37, CPython 3.14.3, NumPy 2.4.2, SciPy 1.17.0, LLVM 21.1.8, libffi 3.5.2, Leptonica 1.87.0, Tesseract 5.5.2, PyTorch 2.10.0, SentencePiece 0.2.1, TensorFlow Lite 2.20.0, TensorRT 10.15.1.29, Triton Inference Server 2.65.0, ONNX 1.20.1, ONNX Runtime 1.23.2, and their dependencies
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* Upgrade presets for OpenCV 4.13.0, FFmpeg 8.0.1, MKL 2025.3.1, DNNL 3.11, OpenBLAS 0.3.31, CUDA 13.1.1, cuDNN 9.19.0.56, NCCL 2.29.3, nvCOMP 5.1.0.21, NVIDIA Video Codec SDK 13.0.37, CPython 3.14.3, NumPy 2.4.2, SciPy 1.17.0, LLVM 21.1.8, libffi 3.5.2, Leptonica 1.87.0, Tesseract 5.5.2, PyTorch 2.10.0, SentencePiece 0.2.1, TensorFlow Lite 2.20.0, TensorRT 10.15.1.29, Triton Inference Server 2.65.0, ONNX 1.20.1, ONNX Runtime 1.24.1, and their dependencies
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### June 30, 2025 version 1.5.12
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* Add presets for the CUFILE module of CUDA

README.md

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* LZ4 1.9.x https://github.com/lz4/lz4
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* MKL 2025.3.x https://software.intel.com/mkl
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* MKL-DNN 0.21.x https://github.com/oneapi-src/oneDNN
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* DNNL 3.10.x https://github.com/oneapi-src/oneDNN
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* DNNL 3.11.x https://github.com/oneapi-src/oneDNN
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* OpenBLAS 0.3.31 http://www.openblas.net/
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* ARPACK-NG 3.9.x https://github.com/opencollab/arpack-ng
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* CMINPACK 1.3.11 https://github.com/devernay/cminpack
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* DepthAI 2.24.x https://github.com/luxonis/depthai-core
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* ONNX 1.20.x https://github.com/onnx/onnx
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* nGraph 0.26.0 https://github.com/NervanaSystems/ngraph
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* ONNX Runtime 1.23.x https://github.com/microsoft/onnxruntime
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* ONNX Runtime 1.24.x https://github.com/microsoft/onnxruntime
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* TVM 0.18.x https://github.com/apache/tvm
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* Bullet Physics SDK 3.25 https://pybullet.org
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* LiquidFun http://google.github.io/liquidfun/

dnnl/README.md

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------------
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This directory contains the JavaCPP Presets module for:
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* DNNL 3.10.2 https://01.org/dnnl
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* DNNL 3.11 https://01.org/dnnl
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Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
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------------
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Here is a simple example of DNNL ported to Java from this C++ source file:
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* https://github.com/oneapi-src/oneDNN/blob/v3.10.2/examples/cnn_inference_int8.cpp
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* https://github.com/oneapi-src/oneDNN/blob/v3.11/examples/cnn_inference_int8.cpp
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We can use [Maven 3](http://maven.apache.org/) to download and install automatically all the class files as well as the native binaries. To run this sample code, after creating the `pom.xml` and `CpuCnnInferenceInt8.java` source files below, simply execute on the command line:
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```bash
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<dependency>
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<groupId>org.bytedeco</groupId>
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<artifactId>dnnl-platform</artifactId>
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<version>3.10.2-1.5.13-SNAPSHOT</version>
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<version>3.11-1.5.13-SNAPSHOT</version>
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</dependency>
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</dependencies>
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<build>

dnnl/cppbuild.sh

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TBB_VERSION=2020.3
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MKLDNN_VERSION=3.10.2
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MKLDNN_VERSION=3.11
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download https://github.com/oneapi-src/oneTBB/archive/v$TBB_VERSION.tar.gz oneTBB-$TBB_VERSION.tar.bz2
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download https://github.com/oneapi-src/oneDNN/archive/v$MKLDNN_VERSION.tar.gz oneDNN-$MKLDNN_VERSION.tar.bz2
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dnnl/platform/pom.xml

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<groupId>org.bytedeco</groupId>
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<artifactId>dnnl-platform</artifactId>
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<version>3.10.2-${project.parent.version}</version>
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<version>3.11-${project.parent.version}</version>
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<name>JavaCPP Presets Platform for DNNL</name>
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<properties>

dnnl/pom.xml

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<groupId>org.bytedeco</groupId>
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<artifactId>dnnl</artifactId>
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<version>3.10.2-${project.parent.version}</version>
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<version>3.11-${project.parent.version}</version>
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<name>JavaCPP Presets for DNNL</name>
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<dependencies>

dnnl/samples/pom.xml

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<version>1.5.13-SNAPSHOT</version>
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<properties>
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<exec.mainClass>CpuCnnInferenceInt8</exec.mainClass>
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<maven.compiler.source>1.7</maven.compiler.source>
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<maven.compiler.target>1.7</maven.compiler.target>
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<maven.compiler.source>1.8</maven.compiler.source>
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<maven.compiler.target>1.8</maven.compiler.target>
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</properties>
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<dependencies>
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<dependency>
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<artifactId>dnnl-platform</artifactId>
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<version>3.11-1.5.13-SNAPSHOT</version>
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</dependency>
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</dependencies>
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<build>

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