update readme and changelong

CHANGELOG.md

@@ -4,6 +4,7 @@
  * 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
 
 ### 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

@@ -23,7 +23,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * Triton Inference Server 2.34.0  https://github.com/triton-inference-server/server
+ * Triton Inference Server 2.38.0  https://github.com/triton-inference-server/server
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -51,9 +51,9 @@ This sample intends to show how to call the Java-mapped C API of Triton to execu
 
  1. Get the source code of Triton Inference Server to prepare the model repository:
 ```bash
- $ wget https://github.com/triton-inference-server/server/archive/refs/tags/v2.34.0.tar.gz
- $ tar zxvf v2.34.0.tar.gz
- $ cd server-2.34.0/docs/examples/model_repository
+ $ wget https://github.com/triton-inference-server/server/archive/refs/tags/v2.38.0.tar.gz
+ $ tar zxvf v2.38.0.tar.gz
+ $ cd server-2.38.0/docs/examples/model_repository
  $ mkdir models
  $ cd models; cp -a ../simple .
 ```