安装Anaconda
在网址https://www.anaconda.com/products/distribution下载安装包,我这里下载的是Anaconda3-2022.05-Linux-x86_64.sh,它对应的Python版本为3.9。
执行
bash Anaconda3-2022.05-Linux-x86_64.sh
安装VIM
sudo apt-get install vim
配置Anaconda环境变量
sudo vim /etc/profile
添加环境配置(此处的user需要替换成你自己的用户名)
export PATH=/home/user/anaconda3/bin:$PATH
使配置可用
source /etc/profile
创建Anaconda虚拟环境
conda create -n py39 python=3.9.0
source activate
source deactivate
conda activate py39
下载英伟达驱动
网址https://www.nvidia.cn/geforce/drivers/
因为我这里是Geforce RTX 3090,所以在下载页面依次填写
搜索后选择一个驱动
我这里下载的文件名为NVIDIA-Linux-x86_64-470.94.run
执行
sudo vim /etc/modprobe.d/blacklist.conf
在末尾添加
blacklist nouveau
保存后继续执行
sudo update-initramfs -u
reboot
重启后重新激活py39
conda activate py39
卸载旧版本的驱动
sudo apt-get purge nvidia*
sudo apt-get autoremove
给下载的run文件赋予可执行权限
sudo chmod 777 NVIDIA-Linux-x86_64-64-470.94.run
sudo apt-get install build-essential
sudo ./NVIDIA-Linux-x86_64-470.94.run -no-x-check -no-nouveau-check -no-opengl-files
检验英伟达显卡是否安装成功
nvidia-smi
出现如上的界面表示安装成功。
安装Cuda
11.4下载地址:https://developer.nvidia.com/cuda-11-4-0-download-archive
如果不是可以去这里找https://developer.nvidia.com/cuda-toolkit-archive
然后选择相应的选项
在终端输入
wget https://developer.download.nvidia.com/compute/cuda/11.4.0/local_installers/cuda_11.4.0_470.42.01_linux.run
chmod 777 cuda_11.4.0_470.42.01_linux.run
sudo ./cuda_11.4.0_470.42.01_linux.run --toolkit --silent --override
验证Cuda是否安装成功
cat /usr/local/cuda-11.4/version.json
如果出现
{
"cuda" : {
"name" : "CUDA SDK",
"version" : "11.4.20210623"
},
"cuda_cudart" : {
"name" : "CUDA Runtime (cudart)",
"version" : "11.4.43"
},
"cuda_cuobjdump" : {
"name" : "cuobjdump",
"version" : "11.4.43"
},
"cuda_cupti" : {
"name" : "CUPTI",
"version" : "11.4.65"
},
"cuda_cuxxfilt" : {
"name" : "CUDA cu++ filt",
"version" : "11.4.43"
},
"cuda_demo_suite" : {
"name" : "CUDA Demo Suite",
"version" : "11.4.43"
},
"cuda_gdb" : {
"name" : "CUDA GDB",
"version" : "11.4.55"
},
"cuda_memcheck" : {
"name" : "CUDA Memcheck",
"version" : "11.4.43"
},
"cuda_nsight" : {
"name" : "Nsight Eclipse Plugins",
"version" : "11.4.43"
},
"cuda_nvcc" : {
"name" : "CUDA NVCC",
"version" : "11.4.48"
},
"cuda_nvdisasm" : {
"name" : "CUDA nvdisasm",
"version" : "11.4.43"
},
"cuda_nvml_dev" : {
"name" : "CUDA NVML Headers",
"version" : "11.4.43"
},
"cuda_nvprof" : {
"name" : "CUDA nvprof",
"version" : "11.4.43"
},
"cuda_nvprune" : {
"name" : "CUDA nvprune",
"version" : "11.4.43"
},
"cuda_nvrtc" : {
"name" : "CUDA NVRTC",
"version" : "11.4.50"
},
"cuda_nvtx" : {
"name" : "CUDA NVTX",
"version" : "11.4.43"
},
"cuda_nvvp" : {
"name" : "CUDA NVVP",
"version" : "11.4.43"
},
"cuda_samples" : {
"name" : "CUDA Samples",
"version" : "11.4.43"
},
"cuda_sanitizer_api" : {
"name" : "CUDA Compute Sanitizer API",
"version" : "11.4.54"
},
"cuda_thrust" : {
"name" : "CUDA Thrust",
"version" : "11.4.43"
},
"libcublas" : {
"name" : "CUDA cuBLAS",
"version" : "11.5.2.43"
},
"libcufft" : {
"name" : "CUDA cuFFT",
"version" : "10.5.0.43"
},
"libcurand" : {
"name" : "CUDA cuRAND",
"version" : "10.2.5.43"
},
"libcusolver" : {
"name" : "CUDA cuSOLVER",
"version" : "11.2.0.43"
},
"libcusparse" : {
"name" : "CUDA cuSPARSE",
"version" : "11.6.0.43"
},
"libnpp" : {
"name" : "CUDA NPP",
"version" : "11.4.0.33"
},
"libnvjpeg" : {
"name" : "CUDA nvJPEG",
"version" : "11.5.1.43"
},
"nsight_compute" : {
"name" : "Nsight Compute",
"version" : "2021.2.0.15"
},
"nsight_systems" : {
"name" : "Nsight Systems",
"version" : "2021.2.4.12"
},
"nvidia_driver" : {
"name" : "NVIDIA Linux Driver",
"version" : "470.42.01"
}
}
表示安装成功
配置环境变量
sudo vim /etc/profile
添加内容如下
export PATH=/usr/local/cuda-11.4/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.4/Lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.4/extras/CUPTI/lib64:$LD_LIBRARY_PATH
使配置可用
source /etc/profile
查看Cuda信息
nvcc -V
显示内容如下
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Wed_Jun__2_19:15:15_PDT_2021
Cuda compilation tools, release 11.4, V11.4.48
Build cuda_11.4.r11.4/compiler.30033411_0
安装cuDNN
安装cuDNN需要英伟达会员登陆,进入页面https://developer.nvidia.com/rdp/cudnn-download
这里我们下载的是Local Installer for Linux x86_64 (Tar)
解压缩
xz -d cudnn-linux-x86_64-8.4.0.27_cuda11.6-archive.tar.xz
tar xvf cudnn-linux-x86_64-8.4.0.27_cuda11.6-archive.tar
将cuDNN的文件拷贝到Cuda目录下
sudo cp cudnn-linux-x86_64-8.4.0.27_cuda11.6-archive/include/cudnn.h /usr/local/cuda-11.4/include/
sudo cp cudnn-linux-x86_64-8.4.0.27_cuda11.6-archive/lib/libcudnn* /usr/local/cuda-11.4/lib64/
sudo chmod 777 /usr/local/cuda-11.4/include/cudnn.h
sudo chmod 777 /usr/local/cuda-11.4/lib64/libcudnn*
安装Pytorch cuda版本
conda activate py39
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
安装PyCharm
进入官网https://www.jetbrains.com/pycharm/download/#section=linux
选择社区版Community
解压缩,启动PyCharm
tar xzvf pycharm-community-2022.1.1.tar.gz
cd /home/user/下载/pycharm-community-2022.1.1/bin
./pycharm.sh
安装Tensorflow cuda版本
在py39虚拟环境下
pip install tensorboardX
pip install tf-nightly-gpu