From 99770a32b9f0cabdef828b3dc73ec9d328747a7d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=A4=A9=E6=B6=AF=E5=8F=A4=E5=B7=B7?= Date: Mon, 30 Sep 2024 22:29:47 +0800 Subject: [PATCH] Update README.md --- README.md | 66 ++++++++++++++++++++++++------------------------------- 1 file changed, 29 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index 0f6ff0e..7b6ae81 100755 --- a/README.md +++ b/README.md @@ -80,69 +80,61 @@ conda activate videoEnv
Linux用户 -
(1) 下载CUDA 11.7
- 
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
-
(2) 安装CUDA 11.7
- 
sudo sh cuda_11.7.0_515.43.04_linux.run
+
(1) 下载CUDA 11.8
+ 
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
+
(2) 安装CUDA 11.8
+ 
sudo sh cuda_11.8.0_520.61.05_linux.run

1. 输入accept

-

2. 选中CUDA Toolkit 11.7（如果你没有安装nvidia驱动则选中Driver，如果你已经安装了nvidia驱动请不要选中driver），之后选中install，回车

+

2. 选中CUDA Toolkit 11.8（如果你没有安装nvidia驱动则选中Driver，如果你已经安装了nvidia驱动请不要选中driver），之后选中install，回车

3. 添加环境变量

在 ~/.bashrc 加入以下内容

# CUDA
-  export PATH=/usr/local/cuda-11.7/bin${PATH:+:${PATH}}
-  export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+ export PATH=/usr/local/cuda-11.8/bin${PATH:+:${PATH}} + export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

使其生效

source ~/.bashrc
-
(3) 下载cuDNN 8.4.1
-

国内：cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz 提取码：57mg

-

国外：cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz

-
(4) 安装cuDNN 8.4.1
- 
 tar -xf cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz
-   mv cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive cuda
-   sudo cp ./cuda/include/* /usr/local/cuda-11.7/include/
-   sudo cp ./cuda/lib/* /usr/local/cuda-11.7/lib64/
-   sudo chmod a+r /usr/local/cuda-11.7/lib64/*
-   sudo chmod a+r /usr/local/cuda-11.7/include/*
+
(3) 下载cuDNN 8.6.0
+

国内：cudnn-linux-x86_64-8.6.0.163_cuda11-archive.tar.xz 提取码：57mg

+

国外：cudnn-linux-x86_64-8.6.0.163_cuda11-archive.tar.xz

+
(4) 安装cuDNN 8.6.0
+ 
 tar -xf cudnn-linux-x86_64-8.6.0.163_cuda11-archive.tar.xz
+   mv cudnn-linux-x86_64-8.6.0.163_cuda11-archive cuda
+   sudo cp ./cuda/include/* /usr/local/cuda-11.8/include/
+   sudo cp ./cuda/lib/* /usr/local/cuda-11.8/lib64/
+   sudo chmod a+r /usr/local/cuda-11.8/lib64/*
+   sudo chmod a+r /usr/local/cuda-11.8/include/*
Windows用户 -
(1) 下载CUDA 11.7
- cuda_11.7.0_516.01_windows.exe -
(2) 安装CUDA 11.7
-
(3) 下载cuDNN 8.2.4
-

cudnn-windows-x64-v8.2.4.15.zip

-
(4) 安装cuDNN 8.2.4
+
(1) 下载CUDA 11.8
+ cuda_11.8.0_522.06_windows.exe +
(2) 安装CUDA 11.8
+
(3) 下载cuDNN 8.6.0
+

cudnn-windows-x86_64-8.6.0.163_cuda11-archive.zip

+
(4) 安装cuDNN 8.6.0

- 将cuDNN解压后的cuda文件夹中的bin, include, lib目录下的文件复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\对应目录下 + 将cuDNN解压后的cuda文件夹中的bin, include, lib目录下的文件复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\对应目录下

- 安装GPU版本Paddlepaddle: - - windows: - - ```shell - python -m pip install paddlepaddle-gpu==2.4.2.post117 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - ``` - - - Linux: - - ```shell - python -m pip install paddlepaddle-gpu==2.4.2.post117 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html - ``` + ```shell + python -m pip install paddlepaddle-gpu==2.6.1 -i https://pypi.tuna.tsinghua.edu.cn/simple + ``` - 安装GPU版本Pytorch: ```shell - conda install pytorch==2.0.1 torchvision==0.16.0 pytorch-cuda=11.8 -c pytorch -c nvidia + conda install pytorch==2.0.1 torchvision==0.15.2 pytorch-cuda=11.8 -c pytorch -c nvidia ``` 或者使用 ```shell - pip install torch==2.0.1 torchvision==0.16.0 --index-url https://download.pytorch.org/whl/cu118 + pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118 ``` - 安装其他依赖: