Fix nits

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

@@ -96,7 +96,7 @@ runs:
         # $SUDO apt-key adv --keyserver keyserver.ubuntu.com --recv-keys BA6932366A755776
         $SUDO apt-get update
         $SUDO apt-get -y install gcc-multilib g++-multilib gfortran-multilib python3 python2.7 python3-minimal python2.7-minimal rpm libasound2-dev:$ARCH freeglut3-dev:$ARCH libfontconfig-dev:$ARCH libgtk2.0-dev:$ARCH libusb-dev:$ARCH libusb-1.0-0-dev:$ARCH libffi-dev:$ARCH libbz2-dev:$ARCH zlib1g-dev:$ARCH libxcb1-dev:$ARCH
-        $SUDO apt-get -y install pkg-config ccache clang $TOOLCHAIN openjdk-8-jdk ant python2 python3-pip swig git file wget unzip tar bzip2 gzip patch autoconf-archive autogen automake make libtool bison flex perl nasm ragel curl libcurl4-openssl-dev libssl-dev libffi-dev libbz2-dev zlib1g-dev rapidjson-dev
+        $SUDO apt-get -y install pkg-config ccache clang $TOOLCHAIN openjdk-8-jdk ant python2 python3-pip swig git file wget unzip tar bzip2 gzip patch autoconf-archive autogen automake cmake make libtool bison flex perl nasm ragel curl libcurl4-openssl-dev libssl-dev libffi-dev libbz2-dev zlib1g-dev rapidjson-dev
 
         export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/
         echo "JAVA_HOME=$JAVA_HOME" >> $GITHUB_ENV

CHANGELOG.md

@@ -3,8 +3,7 @@
  * 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 4.8.0, DNNL 3.2.1, OpenBLAS 0.3.24, CPython 3.11.5, NumPy 1.25.2, SciPy 1.11.2, LLVM 17.0.1, TensorFlow Lite 2.14.0, Triton Inference Server 2.34.0, ONNX 1.14.1, ONNX Runtime 1.16.0, TVM 0.13.0, and their dependencies
- * Upgrade presets for Triton Inference Server 2.38.0
+ * Upgrade presets for OpenCV 4.8.0, DNNL 3.2.1, OpenBLAS 0.3.24, CPython 3.11.5, NumPy 1.25.2, SciPy 1.11.2, LLVM 17.0.1, TensorFlow Lite 2.14.0, Triton Inference Server 2.38.0, ONNX 1.14.1, ONNX Runtime 1.16.0, TVM 0.13.0, 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))

tritonserver/README.md

@@ -61,7 +61,7 @@ Now, this `models` directory will be our model repository.
 
  2. Start the Docker container to run the sample (assuming we are under the `models` directory created above):
 ```bash
- $ docker run -it --gpus=all -v $(pwd):/workspace nvcr.io/nvidia/tritonserver:23.05-py3 bash
+ $ docker run -it --gpus=all -v $(pwd):/workspace nvcr.io/nvidia/tritonserver:23.09-py3 bash
  $ apt update
  $ apt install -y openjdk-11-jdk
  $ wget https://archive.apache.org/dist/maven/maven-3/3.8.4/binaries/apache-maven-3.8.4-bin.tar.gz