Windows 10 + WSL 2 + Ubuntu 22.04 搭建 AI 环境

1. 参考 Enable NVIDIA CUDA on WSL

2. 在WSL里的Ubuntu 22.04中进行以下操作前，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
   

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