- CUDA_VISIBLE_DEVICES=7,6,5,4 python train.py
du -ah --max-depth=1/ # 查看文件大小
cat /usr/local/cuda/version.txt # 查看cuda版本
cat /usr/include/cudnn_version.h # deb方式安装,查看cudnn版本
cat /usr/local/cuda/include/cudnn_version.h # 库安装方式,查看cudnn版本
whereis cudnn_version.h # 查找方法
dpkg -l | grep cudnn # 查看
# 卸载
dpkg -r libcudnn8-samples
dpkg -r libcudnn8-dev
dpkg -r libcudnn8
# cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive根目录
cp -r include/* /usr/local/cuda/include/
cp -r lib/libcudnn* /usr/local/cuda/lib64/
chmod a+r /usr/local/cuda/include/cudnn.h
chmod a+r /usr/local/cuda/lib64/libcudnn*
# 修改完成后,让配置生效
sudo ldconfig
pip install --default-timeout=1000000 torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install --no-cache-dir torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
- 下载:TensorRT-8.4.1.5.Linux.x86_64-gnu.cuda-11.6.cudnn8.4,然后进入文件进行拷贝
pip install --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple pycuda==2022.1
pip install --no-cache-dir python/tensorrt-8.4.1.5-cp38-none-linux_x86_64.whl
pip install --no-cache-dir uff/uff-0.6.9-py2.py3-none-any.whl
pip install --no-cache-dir graphsurgeon/graphsurgeon-0.4.6-py2.py3-none-any.whl
# (1) 配置环境变量:这样系统就可以搜索到TensorRT库(测试好像有问题)
sudo gedit ~/.bashrc
export LD_LIBRARY_PATH=$PATH:/home/PKing/Downloads/TensorRT-8.2.5.1/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=$PATH:/home/PKing/Downloads/TensorRT-8.2.5.1/lib::$LIBRARY_PATH
source ~/.bashrc # 激活
# (2) 也可以将TensorRT相关库直接拷贝到系统目录,这样就不用配置环境变量了 (测试正常)
sudo cp -r lib/* /usr/lib/
sudo cp -r include/* /usr/include/
sudo ldconfig # 修改完成后,让配置生效
pip install tensorrt
pip install nvidia-pyindex
pip install nvidia-tensorrt==8.4.1.5 (版本與TensorRT-8.4.1.5.Linux.x86_64-gnu.cuda-11.6.cudnn8.4匹配即可)
cd <tensorrt installation path>/python
pip install cuda-python
pip install tensorrt-8.6.0-cp310-none-win_amd64.whl
pip install opencv-python
CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 python train.py