curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-docker-keyring.gpg curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sed's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-docke...
为了满足这一需求,我们可以借助Windows Subsystem for Linux (WSL2)和Nvidia-Docker来实现在不同CUDA版本之间进行切换。首先,确保您的系统已安装WSL2和Docker Desktop。如果尚未安装,请按照官方文档进行安装。接下来,安装NVIDIA Container Toolkit。这可以通过运行以下命令完成: wget https://github.com/NVIDIA/nvidia-do...
distribution=$(./etc/os-release;echo$ID$VERSION_ID)curl-s-Lhttps://nvidia.github.io/nvidia-docker/gpgkey|sudogpg--dearmor-o/usr/share/keyrings/nvidia-docker-keyring.gpgcurl-s-Lhttps://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list|sed's#deb https://#deb [signed-by=/...
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-docker-keyring.gpg curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-doc...
https://developer.nvidia.com/cuda/wsl 4.1) 开始安装 Docker curlhttps://get.docker.com|sh\&&sudosystemctl --nowenabledocker 4.2) 开始安装 NVIDIA Container Toolkit distribution=$(./etc/os-release;echo$ID$VERSION_ID)\&&curl-s -L https://nvidia.github.io/nvidia-docker/gpg...
下载地址:https://developer.nvidia.com/cuda/wsl/download GEFORCE DRIVER:https://developer.nvidia.com/46020-gameready-win10-dch-64bit-international 0x01 安装docker和nvidia-docker2 使用Docker安装脚本安装Docker curl https://get.docker.com|sh#sudo usermod -aG docker username ...
curl https://get.docker.com | sh 设置 Use the WSL2 based engine 开启你需要使用docker的wsl发行版 安装CUDA Toolkit 在wsl里,这里举例用到微软store下载的Ubuntu-18.04 sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub ...
sudo apt-get install -ynvidia-docker2 OK,可以跑深度学习了: sudo service docker start docker run --name mytorch --gpus all -it pytorch/pytorch:1.9.0-cuda10.2-cud nn7-runtime bash 进去之后如果显示 The NVIDIA Driver was not detected. GPU functionality will not be available. ...
然而,在 WSL2 中直接使用 NVIDIA GPU 可能会遇到一些困难。幸运的是,NVIDIA Docker 可以作为一种解决方案,让我们在 WSL2 中利用 NVIDIA GPU 进行深度学习等计算任务。一、安装和设置 WSL2首先,确保您的 Windows 10 系统已经启用了 WSL 功能。您可以通过在 Windows 搜索栏中输入“WSL”来找到并启用它。安装完成...
$sudoapt-getinstall-y nvidia-docker2 $sudoservice docker stop $sudoservice docker start 7、运行CUDA Container $ docker run --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark 8、注意事项 基本要求:Cuda的要求高于简单的WSL2安装,且Cuda需要开启用户体验计划,开启后需要及时检查更...