首先，参考：在 WSL 2 中启用 NVIDIA CUDA - Win32 apps | Microsoft Docs 配置一些基础设置。接着进入 wsl 环境进行配置（参考 CUDA 工具包 11.1 下载 | NVIDIA Developer）：
$ wsl
$ sudo apt-get update
$ wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
$ sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
$ wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda-repo-wsl-ubuntu-11-1-local_11.1.0-1_amd64.deb
$ sudo dpkg -i cuda-repo-wsl-ubuntu-11-1-local_11.1.0-1_amd64.deb
$ sudo apt-key add /var/cuda-repo-wsl-ubuntu-11-1-local/7fa2af80.pub
$ sudo apt-get update
$ sudo apt-get -y install cuda
在安装过程中如果报错“404 Not Found [IP: 180.101.196.129 443]”，可以先尝试离线安装：
$ wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run
$ sudo sh cuda_11.1.0_455.23.05_linux.run
【可选】wsl2 安装 GUI 参考：Install GUI Desktop in WSL2 Ubuntu 20.04 LTS in Windows 10 | by Harshit Yadav | Medium & The complete WSL2 + GUI setup. | Medium
安装 Anaconda3
下载 Anaconda | Individual Edition，接着安装：
$ sh Anaconda-...
配置多环境深度学习环境
- 安装 MXNet：
$ conda create -n mxnet python=3.9
$ conda install jupyter notebook
$ conda install cudnn=8 -c conda-forge
$ pip install mxnet-cu110
$ conda install ipykernel
$ python -m ipykernel install --name mxnet --user
$ pip install autopep8
- 安装 TensorFlow：
$ conda create -n tensorflow python=3.9
$ conda install jupyter notebook
$ conda install cudnn=8 -c conda-forge
$ pip install tensorflow
$ conda install ipykernel
$ python -m ipykernel install --name tensorflow --user
$ pip install autopep8
- 安装 PyTorch：
$ conda create -n torch python=3.9
$ conda install jupyter notebook
$ conda install pytorch torchvision torchaudio cudatoolkit=11 -c pytorch -c conda-forge
$ conda install ipykernel
$ python -m ipykernel install --name torch --user
$ pip install autopep8
