WSL搭建深度强化学习环境
https://zhuanlan.zhihu.com/p/683058297
假定你已经安装好wsl
安装miniconda
https://docs.anaconda.com/miniconda/install/
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash ~/Miniconda3-latest-Linux-x86_64.sh
安装过程中需要同意协议，是否自动配置环境变量，最后还可以自定义miniconda安装路径
miniconda更换清华源
https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/
若在最后安装自定义过安装路径，则修改路径下的
.condarc文件
编辑${PATH_TO_INSTALL_PATH}/.condarc
channels:- defaults
show_channel_urls: true
default_channels:- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudpytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
随后清除缓存
conda clean -i
创建conda虚拟环境并激活
一般不同应用或开发以来的python版本不同，本次为深度强化学习单独创建虚拟环境
# 创建python3.9虚拟环境，命名为deep_rl
conda create -n deep_rl python=3.9# 后续使用该环境需要先激活
conda activate deep_rl# 退出虚拟环境
conda deactivate
安装cudatoolkit
https://developer.nvidia.com/cuda-12-1-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pinsudo
mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda-repo-wsl-ubuntu-12-1-local_12.1.1-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-12-1-local_12.1.1-1_amd64.deb
sudo cp /var/cuda-repo-wsl-ubuntu-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda
添加环境变量
# >>> cuda config >>>
# add nvcc compiler to path
export PATH=$PATH:/usr/local/cuda/bin
# add cuBLAS, cuSPARSE, cuRAND, cuSOLVER, cuFFT to path
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
# <<< cuda config <<<
安装pytorch
https://pytorch.org
# 这里以cuda 12.1版本安装为例，需要更换为自己设备cuda的版本
# conda安装
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia# pip安装
# pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
验证是否安装成功
import torch # 如果pytorch安装成功即可导入
print(torch.cuda.is_available()) # 查看CUDA是否可用
print(torch.cuda.device_count()) # 查看可用的CUDA数量
print(torch.version.cuda) # 查看CUDA的版本号
————————————————版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。原文链接：https://blog.csdn.net/qq_41340996/article/details/124326865
