1. 参考 Enable NVIDIA CUDA on WSL

2. 在WSL里的Ubuntu 22.04中进行以下3~8步操作前，请先在 Windows 10 中安装好 Nvidia驱动程序 和 CUDA Toolkit 11.7 ，并将 cuDNN 下载后的文件复制到对应目录中

3. 安装 Conda 23.5.2

wget https://repo.anaconda.com/archive/Anaconda3-2023.07-1-Linux-x86_64.sh
sudo chmod +x ./Anaconda3-2023.07-1-Linux-x86_64.sh
sudo ./Anaconda3-2023.07-1-Linux-x86_64.sh
# yes
# yes
tee -a ~/.condarc << EOF
channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch-lts: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  deepmodeling: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/
EOF
conda clean -i

# Python 3.10.12
conda create -n ai python=3.10
conda activate ai
python3 -c "import platform; print(platform.architecture()[0]); print(platform.machine())"

4. 安装 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
sudo chmod +x ./cuda_11.7.0_515.43.04_linux.run
sudo ./cuda_11.7.0_515.43.04_linux.run
# accept

cd /usr/lib/wsl
sudo mkdir lib2
cd lib2
sudo ln -s ../lib/* .

cd /usr/local/cuda-11.7/targets/x86_64-linux
sudo mkdir lib2
cd lib2
sudo ln -s ../lib/* .
sudo tee /etc/ld.so.conf.d/cuda-11-7.conf << EOF
/usr/local/cuda-11.7/targets/x86_64-linux/lib2
EOF

sudo tee /etc/wsl.conf << EOF
[boot]
systemd=true
command="date '+%Y-%m-%d %H:%M:%S' >> /data/date.log"

[automount]
ldconfig = false
EOF

cd /usr/local/cuda-11.7
sudo ln -s targets/x86_64-linux/lib2 lib64
sudo tee /etc/ld.so.conf << EOF
include /etc/ld.so.conf.d/*.conf
/usr/local/cuda-11.7/lib64
EOF

tee -a ~/.bashrc << EOF
export PATH=/usr/local/cuda-11.7/bin:\$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:\$LD_LIBRARY_PATH
EOF
export PATH=/usr/local/cuda-11.7/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH

sudo ldconfig
nvcc -V
nvidia-smi

5. 复制 cuDNN 8.9.2 文件

# 访问 https://developer.nvidia.com/rdp/cudnn-archive 登录后下载 cudnn-linux-x86_64-8.9.2.26_cuda11-archive.tar.xz
tar xvJf cudnn-linux-x86_64-8.9.2.26_cuda11-archive.tar.xz
cd cudnn-linux-x86_64-8.9.2.26_cuda11-archive
sudo cp include/cudnn.h /usr/local/cuda/include/
sudo cp lib/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

6. 安装 PyTorch 2.0.1

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
python -c "import torch; print(torch.cuda.is_available())"

7. 安装 PaddlePaddle 2.4

conda install paddlepaddle-gpu==2.4.2 cudatoolkit=11.7 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
python -c "import paddle; print(paddle.__file__)"
python -c "import paddle; paddle.utils.run_check();"

8. 安装 jupyter notebook

conda install -n ai ipykernel --update-deps --force-reinstall