'install', '--extra-index-url', 'https://pypi.nvidia.com', 'tensorrt_cu12_libs==10.8.0.43', 'tensorrt_cu12_bindings==10.8.0.43']' returned non-zero exit status 2. 判断为在nvidia服务器上链接失败原因,所以我手动下载这两个依赖,加上镜像源 -i https://pypi.mirrors.ustc.edu.cn/simple/ ...
{OpenCV_LIBS} # OpenCV 库 ${CUDA_LIBRARIES} # CUDA 库 ${CUDA_cublas_LIBRARY} # CUDA BLAS 库 ${CUDA_cudart_LIBRARY} # CUDA Runtime 库 ${CUDA_cudnn_LIBRARY} # CUDA cuDNN 库 ${CUDAToolkit_LIBRARIES} # CUDA Toolkit 库 ) # 设置可执行文件的输出路径 set(EXECUTABLE_OUTPUT_PATH "${C...
1*1*12*20*sizeof(float)));CHECK_CUDA(cudaMalloc(&buffers[6],1*1*6*10*sizeof(float)));...
target_link_libraries(tensorrt ${CUDA_LIBRARIES} ${TENSORRT_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_cudart_static_LIBRARY} ${OpenCV_LIBS}) cmake文件主要注意的几点是cuda和TensorRT动态链接库的查找,只要这几个必备的动态链接库使用cmake查找到就可以编译成功,另外由于我是用了Opencv,所以也将Opencv也加入...
tar -xvzf TensorRT-7.0.0.11.Ubuntu-16.04.x86_64-gnu.cuda-10.0.cudnn7.6.tar.gz export TRT_RELEASE=`pwd`/TensorRT-7.0.0.11 // 环境变量LD_LIBRARY_PATH是系统动态库的查找路径, PATH是系统可执行文件的查找路径 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TRT_RELEASE/lib ...
https://www.lfd.uci.edu/~gohlke/pythonlibs/#pycuda上找到对应CUDA,python的版本。 安装graphsurgeon,onnx_graphsurgeon,uff,python目录下的whl文件。 3、TensorRT测试 简单测试一下 三、TensorRT用例测试 1、首先将.pt转换为.engine:.pt ->.onnx ->.engine 1.1、pt模型转onnx模型代码 代码语言:javascript 代...
set ZLIB_LIBRARY_RELEASE=D:/extlibs/zlib/build-msvc-static/install/lib/zlibstatic.lib set CUDA_HOME=F:\NVIDIA\CUDA\v11.6 set CUDNN_HOME=F:\NVIDIA\CUDNN\v8.5 set TENSORRT_HOME=F:\NVIDIA\TensorRT\v8.4.3.1 set HTTP_PROXY=http://127.0.0.1:10809 rem 你懂的 ...
TensorRT的依赖项(NVIDIA cuDNN和NVIDIA cuBLAS)可能会占用大量的设备内存。TensorRT允许您通过使用构建器配置中的IGpuAllocator(C++,Python)属性来控制这些库是否用于推理。请注意,一些插件实现需要这些库,因此如果排除它们,则网络可能无法成功编译。 TensorRT的依赖项(NVIDIA cuDNN和NVIDIA cuBLAS)可能会占用大量的设备内存...
pip uninstall tensorrt-libs pip uninstall tensorrt-bindings pip uninstall nvidia-cuda-nvrtc-cu11 pip uninstall nvidia-cuda-runtime-cu11 pip uninstall nvidia-cudnn-cu11 pip uninstall nvidia-cublas-cu11 pip uninstall polygraphy #安装 pip install nvidia-cudnn-cu12 ...
The pop up errors are due to PyTorch having their own cudnn libraries, but TensorRT requires them aswell and installs them too. We remove that package after installing the tensorrt wheel. see here: https://github.com/NVIDIA/Stable-Diffusion-WebUI-TensorRT/blob/d8eee382158cd7373e44fe7ac9265...