python 3.6 TensorRT 7.0.0.1 https://developer.nvidia.com/nvidia-tensorrt-7x-download
cd /path/to/TensorRT7.0.0.1 pip install tensorrt-7.0.0.11-cp36-none-linux_x86_64.whlpytorch 1.5 https://pytorch.org/get-started/locally/ 根据机器需求,下载匹配的pytorch cudnn 7.6 https://developer.nvidia.com/rdp/cudnn-archive 下载后,将其解压,放置cuda安装的位置即可 cuda 10.2 https://developer.nvidia.com/cuda-toolkit-archive
wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run sudo sh cuda_10.2.89_440.33.01_linux.run编辑~/.bashrc
export PATH=$PATH:/usr/local/cuda-10.2/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2.0/lib64cuda安装注意事项 一定不要安装默认的驱动,而且options里面也不需要编译opengl的库 nvidia驱动安装 搜索适合的驱动,然后下载 https://www.nvidia.cn/Download/index.aspx?lang=cn
pycuda
pip install pycuda -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.compycharm https://www.jetbrains.com/pycharm/download/#section=linux opencv
pip install opencv-python==3.4.2.17 -i http://mrrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.comnvidia-docker安装 https://www.jianshu.com/p/ef8b0e6c6f5c
4.查看显卡使用情况
watch nvidia-smiCUDA GPU算力对照表: https://developer.nvidia.com/cuda-gpus#collapseOne
Tensorrt C++源码库地址 https://github.com/NVIDIA/TensorRT
sudo cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_BIN_DIR=`pwd`/out -DCMAKE_CUDA_COMPILER:PATH=/usr/local/cuda/bin/nvcc