* Upgrade presets for MKL 2024.2, DNNL 3.6, CMINPACK 1.3.11, Leptoni…

…ca 1.85.0, TensorFlow Lite 2.18.0, TVM 0.18.0
bytedeco · Oct 27, 2024 · 66a75a4 · 66a75a4
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diff --git a/.github/actions/deploy-ubuntu/action.yml b/.github/actions/deploy-ubuntu/action.yml
@@ -228,8 +228,8 @@ runs:
 
         if [[ "$CI_DEPLOY_MODULE" == "mkl" ]]; then
           echo Installing MKL
-          curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/86d6a4c1-c998-4c6b-9fff-ca004e9f7455/l_onemkl_p_2024.0.0.49673_offline.sh
-          $SUDO bash l_onemkl_p_2024.0.0.49673_offline.sh -s -a -s --eula accept
+          curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/89a381f6-f85d-4dda-ae62-30d51470f53c/l_onemkl_p_2024.2.2.17_offline.sh
+          $SUDO bash l_onemkl_p_2024.2.2.17_offline.sh -s -a -s --eula accept
           export MAVEN_OPTIONS="-Djavacpp.platform.compiler=clang++"
         fi
 

diff --git a/.github/actions/deploy-windows/action.yml b/.github/actions/deploy-windows/action.yml
@@ -151,8 +151,8 @@ runs:
 
         if "%CI_DEPLOY_MODULE%"=="mkl" (
           echo Installing MKL
-          curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b5a4ff98-2c98-4b28-87f7-32082cac359e/w_onemkl_p_2024.0.0.49672_offline.exe
-          w_onemkl_p_2024.0.0.49672_offline.exe -s -a -s --eula accept
+          curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9fe96489-78fe-4fea-8cc2-2ddf7de0246a/w_onemkl_p_2024.2.2.16_offline.exe
+          w_onemkl_p_2024.2.2.16_offline.exe -s -a -s --eula accept
         )
 
         if "%CI_DEPLOY_PLATFORM%"=="windows-x86" if "%CI_DEPLOY_MODULE%"=="flycapture" (

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -9,7 +9,7 @@
  * Build FFmpeg with zimg to enable zscale filter ([pull #1481](https://github.com/bytedeco/javacpp-presets/pull/1481))
  * Enable PulseAudio support for FFmpeg on Linux ([pull #1472](https://github.com/bytedeco/javacpp-presets/pull/1472))
  * Virtualize `btCollisionWorld`, `btOverlapFilterCallback`, `btOverlapCallback` from Bullet Physics SDK ([pull #1475](https://github.com/bytedeco/javacpp-presets/pull/1475))
- * Upgrade presets for OpenCV 4.10.0, FFmpeg 7.1, Spinnaker 4.0.0.116 ([pull #1524](https://github.com/bytedeco/javacpp-presets/pull/1524)), DNNL 3.5.3, OpenBLAS 0.3.28, CMINPACK 1.3.9, GSL 2.8, CPython 3.13.0, NumPy 2.1.2, SciPy 1.14.1, LLVM 19.1.2, LibRaw 0.21.2 ([pull #1520](https://github.com/bytedeco/javacpp-presets/pull/1520)), Tesseract 5.4.1, libffi 3.4.6, CUDA 12.6.0, cuDNN 9.3.0, NCCL 2.22.3, nvCOMP 4.0.0, OpenCL 3.0.16, NVIDIA Video Codec SDK 12.2.72, PyTorch 2.5.0 ([pull #1466](https://github.com/bytedeco/javacpp-presets/pull/1466)), SentencePiece 0.2.0, TensorFlow Lite 2.17.0, TensorRT 10.3.0.26, Triton Inference Server 2.48.0, ONNX 1.17.0, ONNX Runtime 1.19.2, TVM 0.17.0, and their dependencies
+ * Upgrade presets for OpenCV 4.10.0, FFmpeg 7.1, Spinnaker 4.0.0.116 ([pull #1524](https://github.com/bytedeco/javacpp-presets/pull/1524)), MKL 2024.2, DNNL 3.6, OpenBLAS 0.3.28, CMINPACK 1.3.11, GSL 2.8, CPython 3.13.0, NumPy 2.1.2, SciPy 1.14.1, LLVM 19.1.2, LibRaw 0.21.2 ([pull #1520](https://github.com/bytedeco/javacpp-presets/pull/1520)), Leptonica 1.85.0, Tesseract 5.4.1, libffi 3.4.6, CUDA 12.6.0, cuDNN 9.3.0, NCCL 2.22.3, nvCOMP 4.0.0, OpenCL 3.0.16, NVIDIA Video Codec SDK 12.2.72, PyTorch 2.5.0 ([pull #1466](https://github.com/bytedeco/javacpp-presets/pull/1466)), SentencePiece 0.2.0, TensorFlow Lite 2.18.0, TensorRT 10.3.0.26, Triton Inference Server 2.48.0, ONNX 1.17.0, ONNX Runtime 1.19.2, TVM 0.18.0, and their dependencies
 
 ### January 29, 2024 version 1.5.10
  * Introduce `macosx-arm64` builds for PyTorch ([pull #1463](https://github.com/bytedeco/javacpp-presets/pull/1463))

diff --git a/README.md b/README.md
@@ -198,10 +198,10 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * LZ4 1.9.x  https://github.com/lz4/lz4
  * MKL 2024.x  https://software.intel.com/mkl
  * MKL-DNN 0.21.x  https://github.com/oneapi-src/oneDNN
- * DNNL 3.5.x  https://github.com/oneapi-src/oneDNN
+ * DNNL 3.6.x  https://github.com/oneapi-src/oneDNN
  * OpenBLAS 0.3.28  http://www.openblas.net/
  * ARPACK-NG 3.9.x  https://github.com/opencollab/arpack-ng
- * CMINPACK 1.3.9  https://github.com/devernay/cminpack
+ * CMINPACK 1.3.11  https://github.com/devernay/cminpack
  * FFTW 3.3.10  http://www.fftw.org/download.html
  * GSL 2.8  http://www.gnu.org/software/gsl/#downloading
  * CPython 3.13.x  https://www.python.org/downloads/
@@ -212,7 +212,7 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * libffi 3.4.x  https://github.com/libffi/libffi
  * libpostal 1.1  https://github.com/openvenues/libpostal
  * LibRaw 0.21.x  https://www.libraw.org/download
- * Leptonica 1.84.x  http://www.leptonica.org/download.html
+ * Leptonica 1.85.x  http://www.leptonica.org/download.html
  * Tesseract 5.4.x  https://github.com/tesseract-ocr/tesseract
  * Caffe 1.0  https://github.com/BVLC/caffe
  * OpenPose 1.7.0  https://github.com/CMU-Perceptual-Computing-Lab/openpose
@@ -226,15 +226,15 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * PyTorch 2.5.x  https://github.com/pytorch/pytorch
  * SentencePiece 0.2.0  https://github.com/google/sentencepiece
  * TensorFlow 1.15.x  https://github.com/tensorflow/tensorflow
- * TensorFlow Lite 2.17.x  https://github.com/tensorflow/tensorflow
+ * TensorFlow Lite 2.18.x  https://github.com/tensorflow/tensorflow
  * TensorRT 10.3.x  https://developer.nvidia.com/tensorrt
  * Triton Inference Server 2.48.x  https://developer.nvidia.com/nvidia-triton-inference-server
  * The Arcade Learning Environment 0.8.x  https://github.com/mgbellemare/Arcade-Learning-Environment
  * DepthAI 2.24.x  https://github.com/luxonis/depthai-core
  * ONNX 1.17.x  https://github.com/onnx/onnx
  * nGraph 0.26.0  https://github.com/NervanaSystems/ngraph
  * ONNX Runtime 1.19.x  https://github.com/microsoft/onnxruntime
- * TVM 0.17.x  https://github.com/apache/tvm
+ * TVM 0.18.x  https://github.com/apache/tvm
  * Bullet Physics SDK 3.25  https://pybullet.org
  * LiquidFun  http://google.github.io/liquidfun/
  * Qt 5.15.x  https://download.qt.io/archive/qt/

diff --git a/cminpack/README.md b/cminpack/README.md
@@ -9,7 +9,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * CMINPACK 1.3.9  http://devernay.free.fr/hacks/cminpack/
+ * CMINPACK 1.3.11  http://devernay.free.fr/hacks/cminpack/
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -46,7 +46,7 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cminpack-platform</artifactId>
-            <version>1.3.9-1.5.11-SNAPSHOT</version>
+            <version>1.3.11-1.5.11-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

diff --git a/cminpack/cminpack.patch b/cminpack/cminpack.patch
@@ -1,6 +1,6 @@
-diff -ruN cminpack-1.3.9/cminpack.h cminpack-1.3.9-patch/cminpack.h
---- cminpack-1.3.9/cminpack.h	2024-05-29 06:09:16.000000000 +0900
-+++ cminpack-1.3.9-patch/cminpack.h	2024-06-16 11:16:11.274115743 +0900
+diff -ruN cminpack-1.3.11/cminpack.h cminpack-1.3.11-patch/cminpack.h
+--- cminpack-1.3.11/cminpack.h	2024-09-13 18:04:15.000000000 +0900
++++ cminpack-1.3.11-patch/cminpack.h	2024-10-27 09:09:37.257671690 +0900
 @@ -36,38 +36,7 @@
  extern "C" {
  #endif /* __cplusplus */
@@ -40,34 +40,54 @@ diff -ruN cminpack-1.3.9/cminpack.h cminpack-1.3.9-patch/cminpack.h
 
  #if defined(__CUDA_ARCH__) || defined(__CUDACC__)
  #define __cminpack_attr__ __device__
-diff -ruN cminpack-1.3.9/cminpackP.h cminpack-1.3.9-patch/cminpackP.h
---- cminpack-1.3.9/cminpackP.h	2024-05-29 06:09:16.000000000 +0900
-+++ cminpack-1.3.9-patch/cminpackP.h	2024-06-16 11:17:44.793475898 +0900
-@@ -33,9 +33,9 @@
+diff -ruN cminpack-1.3.11/cminpackP.h cminpack-1.3.11-patch/cminpackP.h
+--- cminpack-1.3.11/cminpackP.h	2024-09-13 18:04:15.000000000 +0900
++++ cminpack-1.3.11-patch/cminpackP.h	2024-10-27 09:10:43.208509151 +0900
+@@ -11,10 +11,10 @@
  #endif
 
+ #ifdef USE_BLAS
+-#ifdef __APPLE__
+-#include <Accelerate/Accelerate.h>
+-#define __cminpack_blasint__ int
+-#else /* !__APPLE__ */
++//#ifdef __APPLE__
++//#include <Accelerate/Accelerate.h>
++//#define __cminpack_blasint__ int
++//#else /* !__APPLE__ */
+ #ifndef __cminpack_blasint__
+ #define __cminpack_blasint__ int
+ #endif
+@@ -34,13 +34,13 @@
+   const char *TransA, const char *Diag,
+   const __cminpack_blasint__ *N, const __cminpack_real__ *A, const __cminpack_blasint__ *lda, __cminpack_real__ *X,
+   const __cminpack_blasint__ *incX);
+-#endif /* !__APPLE__ */
++//#endif /* !__APPLE__ */
+ #endif /* USE_BLAS */
+
  #ifdef USE_LAPACK
 -#ifdef __APPLE__
 -#include <Accelerate/Accelerate.h>
--#else
+-#else /* !__APPLE__ */
 +//#ifdef __APPLE__
 +//#include <Accelerate/Accelerate.h>
-+//#else
++//#else /* !__APPLE__ */
  #if defined(__LP64__) /* In LP64 match sizes with the 32 bit ABI */
  typedef int 		__CLPK_integer;
  typedef int 		__CLPK_logical;
-@@ -58,7 +58,7 @@
+@@ -62,7 +62,7 @@
+ void __cminpack_lapack__(geqrf_)(
    __CLPK_integer *m, __CLPK_integer *n, __cminpack_real__ *a, __CLPK_integer * lda,
    __cminpack_real__ *tau, __cminpack_real__ *work, __CLPK_integer *lwork, __CLPK_integer *info);
+-#endif /* !__APPLE__ */
++//#endif /* !__APPLE__ */
  #endif
--#endif
-+//#endif
 
  #include "minpackP.h"
-
-diff -ruN cminpack-1.3.9/Makefile cminpack-1.3.9-patch/Makefile
---- cminpack-1.3.9/Makefile	2024-05-29 06:09:16.000000000 +0900
-+++ cminpack-1.3.9-patch/Makefile	2024-06-16 11:15:38.626990012 +0900
+diff -ruN cminpack-1.3.11/Makefile cminpack-1.3.11-patch/Makefile
+--- cminpack-1.3.11/Makefile	2024-09-13 18:04:15.000000000 +0900
++++ cminpack-1.3.11-patch/Makefile	2024-10-27 09:09:37.259671695 +0900
 @@ -11,7 +11,7 @@
  #LIBSUFFIX=s
  #CFLAGS="-O3 -g -Wall -Wextra -DUSE_BLAS -DUSE_LAPACK"

diff --git a/cminpack/cppbuild.sh b/cminpack/cppbuild.sh
@@ -7,7 +7,7 @@ if [[ -z "$PLATFORM" ]]; then
     exit
 fi
 
-CMINPACK_VERSION=1.3.9
+CMINPACK_VERSION=1.3.11
 download https://github.com/devernay/cminpack/archive/v$CMINPACK_VERSION.tar.gz cminpack-$CMINPACK_VERSION.tar.gz
 
 mkdir -p $PLATFORM

diff --git a/cminpack/platform/pom.xml b/cminpack/platform/pom.xml
@@ -12,7 +12,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>cminpack-platform</artifactId>
-  <version>1.3.9-${project.parent.version}</version>
+  <version>1.3.11-${project.parent.version}</version>
   <name>JavaCPP Presets Platform for CMINPACK</name>
 
   <properties>

diff --git a/cminpack/pom.xml b/cminpack/pom.xml
@@ -11,7 +11,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>cminpack</artifactId>
-  <version>1.3.9-${project.parent.version}</version>
+  <version>1.3.11-${project.parent.version}</version>
   <name>JavaCPP Presets for CMINPACK</name>
 
   <dependencies>

diff --git a/cminpack/samples/pom.xml b/cminpack/samples/pom.xml
@@ -12,7 +12,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cminpack-platform</artifactId>
-            <version>1.3.9-1.5.11-SNAPSHOT</version>
+            <version>1.3.11-1.5.11-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

diff --git a/dnnl/README.md b/dnnl/README.md
@@ -9,7 +9,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * DNNL 3.5.3  https://01.org/dnnl
+ * DNNL 3.6  https://01.org/dnnl
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -25,7 +25,7 @@ Sample Usage
 ------------
 Here is a simple example of DNNL ported to Java from this C++ source file:
 
- * https://github.com/oneapi-src/oneDNN/blob/v3.5.3/examples/cnn_inference_int8.cpp
+ * https://github.com/oneapi-src/oneDNN/blob/v3.6/examples/cnn_inference_int8.cpp
 
 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:
 ```bash
@@ -38,15 +38,15 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
     <modelVersion>4.0.0</modelVersion>
     <groupId>org.bytedeco.dnnl</groupId>
     <artifactId>samples</artifactId>
-    <version>1.5.10</version>
+    <version>1.5.11-SNAPSHOT</version>
     <properties>
         <exec.mainClass>CpuCnnInferenceInt8</exec.mainClass>
     </properties>
     <dependencies>
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>dnnl-platform</artifactId>
-            <version>3.5.3-1.5.11-SNAPSHOT</version>
+            <version>3.6-1.5.11-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

diff --git a/dnnl/cppbuild.sh b/dnnl/cppbuild.sh
@@ -11,7 +11,7 @@ export DNNL_CPU_RUNTIME="OMP" # or TBB
 export DNNL_GPU_RUNTIME="OCL"
 
 TBB_VERSION=2020.3
-MKLDNN_VERSION=3.5.3
+MKLDNN_VERSION=3.6
 download https://github.com/oneapi-src/oneTBB/archive/v$TBB_VERSION.tar.gz oneTBB-$TBB_VERSION.tar.bz2
 download https://github.com/oneapi-src/oneDNN/archive/v$MKLDNN_VERSION.tar.gz oneDNN-$MKLDNN_VERSION.tar.bz2
 

diff --git a/dnnl/platform/pom.xml b/dnnl/platform/pom.xml
@@ -12,7 +12,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>dnnl-platform</artifactId>
-  <version>3.5.3-${project.parent.version}</version>
+  <version>3.6-${project.parent.version}</version>
   <name>JavaCPP Presets Platform for DNNL</name>
 
   <properties>

diff --git a/dnnl/pom.xml b/dnnl/pom.xml
@@ -11,7 +11,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>dnnl</artifactId>
-  <version>3.5.3-${project.parent.version}</version>
+  <version>3.6-${project.parent.version}</version>
   <name>JavaCPP Presets for DNNL</name>
 
   <dependencies>

diff --git a/dnnl/samples/pom.xml b/dnnl/samples/pom.xml
@@ -12,7 +12,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>dnnl-platform</artifactId>
-            <version>3.5.3-1.5.11-SNAPSHOT</version>
+            <version>3.6-1.5.11-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>