* Upgrade presets for MKL 2024.0, DNNL 3.3.3, CPython 3.12.1

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

@@ -214,8 +214,8 @@ runs:
 
         if [[ "$CI_DEPLOY_MODULE" == "mkl" ]]; then
           echo Installing MKL
-          curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/cd17b7fe-500e-4305-a89b-bd5b42bfd9f8/l_onemkl_p_2023.1.0.46342_offline.sh
-          $SUDO bash l_onemkl_p_2023.1.0.46342_offline.sh -s -a -s --eula accept
+          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
           export MAVEN_OPTIONS="-Djavacpp.platform.compiler=clang++"
 
           # Allocate a swapfile on Linux as it's not enabled by default

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

@@ -15,6 +15,8 @@ runs:
         C:\msys64\usr\bin\bash -lc "pacman -S --needed --noconfirm mingw-w64-x86_64-nasm mingw-w64-x86_64-toolchain mingw-w64-x86_64-libtool mingw-w64-x86_64-gcc mingw-w64-i686-gcc mingw-w64-x86_64-gcc-fortran mingw-w64-i686-gcc-fortran mingw-w64-x86_64-libwinpthread-git mingw-w64-i686-libwinpthread-git mingw-w64-x86_64-SDL2 mingw-w64-i686-SDL2 mingw-w64-x86_64-ragel"
         set "PATH=C:\hostedtoolcache\windows\Python\3.9.13\x64;C:\msys64\usr\bin;%PATH%"
 
+        C:\msys64\usr\bin\bash -lc "pacman -Q"
+
         echo Installing Windows SDK 8.1
         curl -Lo sdksetup.exe https://go.microsoft.com/fwlink/p/?LinkId=323507
         sdksetup.exe /features OptionId.WindowsDesktopSoftwareDevelopmentKit OptionId.NetFxSoftwareDevelopmentKit /quiet
@@ -148,10 +150,8 @@ runs:
 
         if "%CI_DEPLOY_MODULE%"=="mkl" (
           echo Installing MKL
-          curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/e0195cbc-2c9b-4489-9542-c1447e70df30/w_onemkl_p_2023.1.0.46356_offline.exe
-          curl -LO https://registrationcenter-download.intel.com/akdlm/IRC_NAS/c961e083-5685-4f0b-ada5-c6cf16f561dd/w_BaseKit_32bit_p_2023.1.0.46402_offline.exe
-          w_onemkl_p_2023.1.0.46356_offline.exe -s -a -s --eula accept
-          w_BaseKit_32bit_p_2023.1.0.46402_offline.exe -s -a -s --eula accept
+          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
         )
 
         if "%CI_DEPLOY_PLATFORM%"=="windows-x86" if "%CI_DEPLOY_MODULE%"=="flycapture" (

.github/workflows/mkl.yml

@@ -25,10 +25,10 @@ jobs:
     runs-on: ubuntu-20.04
     steps:
       - uses: bytedeco/javacpp-presets/.github/actions/deploy-ubuntu@actions
-  macosx-x86_64:
-    runs-on: macos-11
-    steps:
-      - uses: bytedeco/javacpp-presets/.github/actions/deploy-macosx@actions
+#  macosx-x86_64:
+#    runs-on: macos-11
+#    steps:
+#      - uses: bytedeco/javacpp-presets/.github/actions/deploy-macosx@actions
 #  windows-x86:
 #    runs-on: windows-2019
 #    steps:
@@ -39,7 +39,7 @@ jobs:
       - uses: bytedeco/javacpp-presets/.github/actions/deploy-windows@actions
   redeploy:
 #    needs: [linux-x86, linux-x86_64, macosx-x86_64, windows-x86, windows-x86_64]
-    needs: [linux-x86_64, macosx-x86_64, windows-x86_64]
+    needs: [linux-x86_64, windows-x86_64]
     runs-on: ubuntu-20.04
     steps:
       - uses: bytedeco/javacpp-presets/.github/actions/redeploy@actions

CHANGELOG.md

@@ -5,7 +5,7 @@
  * Refactor and improve presets for PyTorch ([pull #1360](https://github.com/bytedeco/javacpp-presets/pull/1360))
  * Include `mkl_lapack.h` header file in presets for MKL ([issue #1388](https://github.com/bytedeco/javacpp-presets/issues/1388))
  * Map new higher-level C++ API of Triton Inference Server ([pull #1361](https://github.com/bytedeco/javacpp-presets/pull/1361))
- * Upgrade presets for OpenCV 4.8.1, FFmpeg 6.1, HDF5 1.14.3, DNNL 3.3.2, OpenBLAS 0.3.25, ARPACK-NG 3.9.1, CPython 3.12.0, NumPy 1.26.2, SciPy 1.11.4, LLVM 17.0.6, Leptonica 1.83.1, Tesseract 5.3.3, CUDA 12.3.0, cuDNN 8.9.5, NCCL 2.18.5, PyTorch 2.1.2 ([pull #1426](https://github.com/bytedeco/javacpp-presets/pull/1426)), TensorFlow Lite 2.15.0, Triton Inference Server 2.38.0, DepthAI 2.23.0, ONNX 1.15.0, ONNX Runtime 1.16.3, TVM 0.14.0, and their dependencies
+ * Upgrade presets for OpenCV 4.8.1, FFmpeg 6.1, HDF5 1.14.3, MKL 2024.0, DNNL 3.3.3, OpenBLAS 0.3.25, ARPACK-NG 3.9.1, CPython 3.12.1, NumPy 1.26.2, SciPy 1.11.4, LLVM 17.0.6, Leptonica 1.83.1, Tesseract 5.3.3, CUDA 12.3.0, cuDNN 8.9.5, NCCL 2.18.5, PyTorch 2.1.2 ([pull #1426](https://github.com/bytedeco/javacpp-presets/pull/1426)), TensorFlow Lite 2.15.0, Triton Inference Server 2.38.0, DepthAI 2.23.0, ONNX 1.15.0, ONNX Runtime 1.16.3, TVM 0.14.0, and their dependencies
 
 ### June 6, 2023 version 1.5.9
  * Virtualize `nvinfer1::IGpuAllocator` from TensorRT to allow customization ([pull #1367](https://github.com/bytedeco/javacpp-presets/pull/1367))

README.md

@@ -196,7 +196,7 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * HDF5 1.14.x  https://www.hdfgroup.org/downloads/
  * Hyperscan 5.4.x  https://github.com/intel/hyperscan
  * LZ4 1.9.x  https://github.com/lz4/lz4
- * MKL 2023.x  https://software.intel.com/mkl
+ * MKL 2024.x  https://software.intel.com/mkl
  * MKL-DNN 0.21.x  https://github.com/oneapi-src/oneDNN
  * DNNL 3.3.x  https://github.com/oneapi-src/oneDNN
  * OpenBLAS 0.3.25  http://www.openblas.net/

cpython/README.md

@@ -14,7 +14,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * CPython 3.12.0  https://www.python.org/
+ * CPython 3.12.1  https://www.python.org/
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -54,7 +54,7 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cpython-platform</artifactId>
-            <version>3.12.0-1.5.10-SNAPSHOT</version>
+            <version>3.12.1-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

cpython/cppbuild.sh

@@ -8,7 +8,7 @@ if [[ -z "$PLATFORM" ]]; then
 fi
 
 OPENSSL=openssl-3.1.4
-CPYTHON_VERSION=3.12.0
+CPYTHON_VERSION=3.12.1
 download https://www.openssl.org/source/$OPENSSL.tar.gz $OPENSSL.tar.gz
 download https://www.python.org/ftp/python/$CPYTHON_VERSION/Python-$CPYTHON_VERSION.tgz Python-$CPYTHON_VERSION.tgz
 

cpython/platform/pom.xml

@@ -12,7 +12,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>cpython-platform</artifactId>
-  <version>3.12.0-${project.parent.version}</version>
+  <version>3.12.1-${project.parent.version}</version>
   <name>JavaCPP Presets Platform for CPython</name>
 
   <properties>

cpython/pom.xml

@@ -11,7 +11,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>cpython</artifactId>
-  <version>3.12.0-${project.parent.version}</version>
+  <version>3.12.1-${project.parent.version}</version>
   <name>JavaCPP Presets for CPython</name>
 
   <dependencies>

cpython/samples/pom.xml

@@ -12,7 +12,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cpython-platform</artifactId>
-            <version>3.12.0-1.5.10-SNAPSHOT</version>
+            <version>3.12.1-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

cpython/src/gen/java/org/bytedeco/cpython/PyASCIIObject.java

@@ -124,6 +124,8 @@ See also _PyUnicode_CheckConsistency().
            and the kind is PyUnicode_1BYTE_KIND. If ascii is set and compact is
            set, use the PyASCIIObject structure. */
         @Name("state.ascii") public native @Cast("unsigned int") @NoOffset int state_ascii(); public native PyASCIIObject state_ascii(int setter);
+        /* The object is statically allocated. */
+        @Name("state.statically_allocated") public native @Cast("unsigned int") @NoOffset int state_statically_allocated(); public native PyASCIIObject state_statically_allocated(int setter);
         /* Padding to ensure that PyUnicode_DATA() is always aligned to
            4 bytes (see issue #19537 on m68k). */
 

cpython/src/gen/java/org/bytedeco/cpython/PyModuleDef.java

@@ -10,8 +10,7 @@
 
 import static org.bytedeco.cpython.global.python.*;
 
-
-// #endif /* New in 3.5 */
+// #endif
 
 @Properties(inherit = org.bytedeco.cpython.presets.python.class)
 public class PyModuleDef extends Pointer {

cpython/src/gen/java/org/bytedeco/cpython/global/python.java

@@ -144,12 +144,12 @@ There is also (independent) API version information in modsupport.h.
 /*--start constants--*/
 public static final int PY_MAJOR_VERSION =        3;
 public static final int PY_MINOR_VERSION =        12;
-public static final int PY_MICRO_VERSION =        0;
+public static final int PY_MICRO_VERSION =        1;
 public static final int PY_RELEASE_LEVEL =        PY_RELEASE_LEVEL_FINAL;
 public static final int PY_RELEASE_SERIAL =       0;
 
 /* Version as a string */
-public static final String PY_VERSION =              "3.12.0";
+public static final String PY_VERSION =              "3.12.1";
 /*--end constants--*/
 
 /* Version as a single 4-byte hex number, e.g. 0x010502B2 == 1.5.2b2.
@@ -1155,6 +1155,9 @@ There is also (independent) API version information in modsupport.h.
 /* Define if you can turn off readline's signal handling. */
 /* #undef HAVE_RL_CATCH_SIGNAL */
 
+/* Define if readline supports rl_compdisp_func_t */
+/* #undef HAVE_RL_COMPDISP_FUNC_T */
+
 /* Define if you have readline 2.2 */
 /* #undef HAVE_RL_COMPLETION_APPEND_CHARACTER */
 
@@ -1784,6 +1787,9 @@ There is also (independent) API version information in modsupport.h.
 /* Define if you want to enable internal statistics gathering. */
 /* #undef Py_STATS */
 
+/* The version of SunOS/Solaris as reported by `uname -r' without the dot. */
+/* #undef Py_SUNOS_VERSION */
+
 /* Define if you want to enable tracing references for debugging purpose */
 /* #undef Py_TRACE_REFS */
 
@@ -8012,6 +8018,8 @@ and where the hash values are equal (i.e. a very probable match) */
 
 /* Long (arbitrary precision) integer object interface */
 
+// PyLong_Type is declared by object.h
+
 // #define PyLong_Check(op)
 //         PyType_FastSubclass(Py_TYPE(op), Py_TPFLAGS_LONG_SUBCLASS)
 // #define PyLong_CheckExact(op) Py_IS_TYPE((op), &PyLong_Type)
@@ -8211,6 +8219,9 @@ and where the hash values are equal (i.e. a very probable match) */
 // #ifdef __cplusplus
 // #endif
 
+
+// PyBool_Type is declared by object.h
+
 // #define PyBool_Check(x) Py_IS_TYPE((x), &PyBool_Type)
 
 /* Py_False and Py_True are the only two bools in existence. */
@@ -9166,13 +9177,18 @@ of a key wins, else the first.  The Python dict constructor dict(seq2)
 
 public static final int Py_mod_create = 1;
 public static final int Py_mod_exec = 2;
+// #if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x030c0000
 public static final int Py_mod_multiple_interpreters = 3;
+// #endif
 
 // #ifndef Py_LIMITED_API
 public static final int _Py_mod_LAST_SLOT = 3;
 // #endif
 
+// #endif /* New in 3.5 */
+
 /* for Py_mod_multiple_interpreters: */
+// #if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x030c0000
 public static native @MemberGetter Pointer Py_MOD_MULTIPLE_INTERPRETERS_NOT_SUPPORTED();
 public static final Pointer Py_MOD_MULTIPLE_INTERPRETERS_NOT_SUPPORTED = Py_MOD_MULTIPLE_INTERPRETERS_NOT_SUPPORTED();
 public static native @MemberGetter Pointer Py_MOD_MULTIPLE_INTERPRETERS_SUPPORTED();
@@ -13831,9 +13847,6 @@ use PyFrame_GetLineNumber() instead. */
 public static final int Py_eval_input = 258;
 public static final int Py_func_type_input = 345;
 
-/* This doesn't need to match anything */
-public static final int Py_fstring_input = 800;
-
 // #ifndef Py_LIMITED_API
 // #  define Py_CPYTHON_COMPILE_H
 // #  include "cpython/compile.h"

dnnl/README.md

@@ -9,7 +9,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * DNNL 3.3.2  https://01.org/dnnl
+ * DNNL 3.3.3  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/v3.3.2/examples/cnn_inference_int8.cpp
+ * https://github.com/oneapi-src/oneDNN/blob/v3.3.3/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
@@ -46,7 +46,7 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>dnnl-platform</artifactId>
-            <version>3.3.2-1.5.10-SNAPSHOT</version>
+            <version>3.3.3-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

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=3.3.2
+MKLDNN_VERSION=3.3.3
 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
 

dnnl/platform/pom.xml

@@ -12,7 +12,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>dnnl-platform</artifactId>
-  <version>3.3.2-${project.parent.version}</version>
+  <version>3.3.3-${project.parent.version}</version>
   <name>JavaCPP Presets Platform for DNNL</name>
 
   <properties>

dnnl/pom.xml

@@ -11,7 +11,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>dnnl</artifactId>
-  <version>3.3.2-${project.parent.version}</version>
+  <version>3.3.3-${project.parent.version}</version>
   <name>JavaCPP Presets for DNNL</name>
 
   <dependencies>

dnnl/samples/pom.xml

@@ -12,7 +12,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>dnnl-platform</artifactId>
-            <version>3.3.2-1.5.10-SNAPSHOT</version>
+            <version>3.3.3-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

dnnl/src/gen/java/org/bytedeco/dnnl/global/dnnl.java

@@ -3475,7 +3475,7 @@ public class dnnl extends org.bytedeco.dnnl.presets.dnnl {
 public static final int DNNL_VERSION_MINOR = 3;
 
 /** Patch version */
-public static final int DNNL_VERSION_PATCH = 2;
+public static final int DNNL_VERSION_PATCH = 3;
 
 /** Git commit hash */
 public static native @MemberGetter String DNNL_VERSION_HASH();

gsl/README.md

@@ -38,22 +38,22 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
     <modelVersion>4.0.0</modelVersion>
     <groupId>org.bytedeco.gsl</groupId>
     <artifactId>demo</artifactId>
-    <version>1.5.9</version>
+    <version>1.5.10-SNAPSHOT</version>
     <properties>
         <exec.mainClass>Demo</exec.mainClass>
     </properties>
     <dependencies>
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>gsl-platform</artifactId>
-            <version>2.7-1.5.9</version>
+            <version>2.7-1.5.10-SNAPSHOT</version>
         </dependency>
 
         <!-- Additional dependencies to use bundled full version of MKL -->
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>mkl-platform-redist</artifactId>
-            <version>2023.1-1.5.9</version>
+            <version>2024.0-1.5.10-SNAPSHOT</version>
         </dependency>
 
     </dependencies>

gsl/samples/pom.xml

@@ -19,7 +19,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>mkl-platform-redist</artifactId>
-            <version>2023.1-1.5.10-SNAPSHOT</version>
+            <version>2024.0-1.5.10-SNAPSHOT</version>
         </dependency>
 
     </dependencies>

llvm/samples/polly/pom.xml

@@ -23,12 +23,12 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>mkl-platform</artifactId>
-            <version>2023.1-1.5.10-SNAPSHOT</version>
+            <version>2024.0-1.5.10-SNAPSHOT</version>
         </dependency>
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>mkl-platform-redist</artifactId>
-            <version>2023.1-1.5.10-SNAPSHOT</version>
+            <version>2024.0-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

mkl/README.md

@@ -9,7 +9,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * MKL 2023.1.0  https://software.intel.com/mkl
+ * MKL 2024.0.0  https://software.intel.com/mkl
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -40,22 +40,22 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
     <modelVersion>4.0.0</modelVersion>
     <groupId>org.bytedeco.mkl</groupId>
     <artifactId>mkl</artifactId>
-    <version>1.5.9</version>
+    <version>1.5.10-SNAPSHOT</version>
     <properties>
         <exec.mainClass>DGEMMExample</exec.mainClass>
     </properties>
     <dependencies>
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>mkl-platform</artifactId>
-            <version>2023.1-1.5.9</version>
+            <version>2024.0-1.5.10-SNAPSHOT</version>
         </dependency>
 
         <!-- Additional dependencies to use bundled full version of MKL -->
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>mkl-platform-redist</artifactId>
-            <version>2023.1-1.5.9</version>
+            <version>2024.0-1.5.10-SNAPSHOT</version>
         </dependency>
 
     </dependencies>