RTX3080+pytorch-gpu+Linux环境

一、nvidia官网下载nvidia-driver455.23以上，cuda11.1,cudnn-v8.0.4.30二、pytorch+torchvision源码编译三、tensorflow安装

一、nvidia官网下载nvidia-driver455.23以上，cuda11.1,cudnn-v8.0.4.30

确保nvidia-driver、cuda11.1、cudnn8.0.4.30安装成功

NVIDIA-Linux-x86_64-455.23.04.runcuda_11.1.0_455.23.05_linux.runcudnn-11.1-linux-x64-v8.0.4.30.tgz/8.0.5.39上述文件百度网盘下载链接nvidia-cuda-cudnn 密码: paat，nvidia-driver本人已经更新到455.45.01，cudnn:8.0.5.39

二、pytorch+torchvision源码编译

conda create -n pytorch python=3.7

pip install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses

git clone --recursive https://github.com/pytorch/pytorch cd pytorch git submodule sync git submodule update --init --recursive export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"} python setup.py install

成功之后重启终端

python >>> import torch >>> torch.__version__ '1.8.0a0+3d421b3' >>> torch.cuda.is_available() True

git clone --recursive https://github.com/pytorch/vision.git

cd vision/ python setup.py install

重启终端

>>> import torchvision >>> torchvision.__version__ '0.8.0a0+a9c78f1' >>>

三、tensorflow安装

pip install tf-nightly-gpu>>> tf.__version__ '2.4.0-dev20201014'