[Python知识库]wsl2安装cuda及pytorch

1、在windows11上安装cuda11.1
使用 nvidia-smi和nvcc --version查看是否安装成功以及版本

2、使用wsl新建Ubuntu-20.04系统

3、检查/usr/lib/wsl/lib是否存在nvidia-smi以及若干cuda.co

cd /usr/lib/wsl/lib
ls
# 如果输出以下内容就算成功。否则重新在宿主机上更新驱动。
libcuda.so      libd3d12.so      libnvcuvid.so        libnvidia-encode.so    libnvidia-opticalflow.so    nvidia-smi
libcuda.so.1    libd3d12core.so  libnvcuvid.so.1      libnvidia-encode.so.1  libnvidia-opticalflow.so.1
libcuda.so.1.1  libdxcore.so     libnvdxdlkernels.so  libnvidia-ml.so.1      libnvwgf2umx.so

这一步非常重要，如果没有这些so文件，那么后面pytorch就没法检测到cuda存在。

# 执行nvidia-smi
(openmmlab) root@DESKTOP-7505DGE:/usr/lib/wsl/lib# ./nvidia-smi
Mon Sep 12 14:28:30 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01    Driver Version: 516.94       CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  On   | 00000000:0A:00.0  On |                  N/A |
|  0%   38C    P8    14W / 250W |    989MiB / 11264MiB |      3%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

4、根据CUDA Support for WSL 2 为wsl准备环境

sudo apt-key del 7fa2af80
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

5、安装好后去更新环境变量

vim ~/.bashrc

# 加入以下两行
# export PATH=/usr/lib/wsl/lib/
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

# 退出后执行以下两行，如果有输出，则说明环境配置成功
source ~/.bashrc
nvcc --version 
# 输出
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Tue_Sep_15_19:10:02_PDT_2020
Cuda compilation tools, release 11.1, V11.1.74
Build cuda_11.1.TC455_06.29069683_0

6、安装anaconda

# 下载anaconda安装包，然后cd到sh文件位置处执行以下命令
bash Anaconda.sh
# 安装完成后执行
source ~/.bashrc

7、创建conda环境

conda create --name openmmlab python=3.7 -y
conda activate openmmlab

conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=11.1 -c pytorch -c conda-forge

8、检查cuda是否可用

# 进入python命令行输入以下命令
>>> import torch
>>> torch.cuda.is_available()
True

9、如果c盘空间不足，可以参考Move WSL to Another Drive将wsl2移动到D盘

D:
mkdir WSL
cd WSL
wsl --export Ubuntu-20.04 ubuntu-20.04.tar
wsl --unregister Ubuntu-20.04
mkdir Ubuntu-20.04
wsl --import Ubuntu-20.04 Ubuntu-20.04 ubuntu-20.04.tar 

参考资料：
https://docs.nvidia.com/cuda/wsl-user-guide/index.html
https://docs.microsoft.com/en-us/windows/wsl/tutorials/gpu-compute