原本一开始有人说是驱动版本问题 我nvcc -V是ok的,但是nvidia-smi一直报错,Command ‘nvidia-smi’ not found, but can be installed with: 解决 cp /usr/lib/wsl/lib/nvidia-smi /usr/bin/nvidia-smi chmod ogu+x /usr/bin/nvidia-smi 在wsl2中运行这两个语句,再输入nvidia-smi,成功了 ——— 版权声...
在我的桌面上,nvidia-smi和CUDA的示例在WSL2中工作得很好。但是在我的笔记本电脑上,运行nvidia-smiin WSL2会返回: 代码语言:javascript 复制 $ nvidia-smi Failed to initializeNVML:GPUaccess blocked by the operating system Failed to properly shut downNVML:GPUaccess blocked by the operating system 我知道我...
I have new windows server where I have installed WSL2 for GPU pass through, but nvidia-smi command is not working and it gives NVML initialization : unknown error whereas nvidia-smi.exe is working from the windows sideHowever, It works on another server with similar ...
更新完成后可以在命令行/wsl中输入nvidia-smi 可以看到输出 这里的CUDA Version指的是该驱动版本最高可支持的CUDA版本 安装CUDA 到NVIDIA官网下载符合条件的CUDA 这里我一开始直接选择安装了最新版的CUDA 12.4,随后发现Tensorflow 目前(2024.3.17) 并不支持 12.4,于是重新安装,选择了CUDA 11.2 直接下载EXE版本安装即可 ...
Download the latest official NVIDIA drivers 输入nvidia-smi,查验是否安装成功。WSL2里面啥都不用做,在WSL2命令行直接就能查看nvidia-smi。启动docker也能一样查看 winse@DESKTOP-BR4MG38:stable-diffusion-taiyi$ docker run -it --rm --gpus all ubuntu nvidia-smi ...
I am using an Ubuntu 18.04 LTS distribution with WSL2. The distribution comes with Nvidia drivers as a pre-installed virtual packages. However the program I was trying to run can only use CPU graphics, and an Nvidia driver will cause it ...
I am doing some dnn inferencing using onnx model and OpenCV. I’ve noticed big differences in inference speed on Docker compared to WSL2. Both are using Ubuntu 20.04. I’ve run nvidia-smi while the application was running…
这里安装Tensorflow和Pytorch两个主流的机器学习包,并配置好CUDA和CUDNN的加速。Ubuntu本身就会带Nvidia显卡的驱动,可以使用命令nvidia-smi查看显卡的信息。首先安装CUDA和CUDNN。使用conda search cudatoolkit查看可安装的版本,可以看到并不全。我的显卡是RTX3070,所以考虑使用cuda11.3,这也是兼容大多数包的版本。
Hello, i tried testing an app with CUDA on WSL2, on Windows it worked perfectly fine but in WSL it doesn't detect a Cuda device but only the CPU accelerator. The nvidia deviceQuery sample and nvidia-smi detect my GPU fine and can run CUDA on it. here is my test code: using ILGPU...
nvidia-smi # 版本更新 sudo apt update-y sudo apt install vim wget curl git-y # WSL2 开启Systemd sudo vim/etc/wsl.conf [boot] systemd=true Windows PowerShell下运行 (关闭后重启) wsl--shutdown # Docker 安装 curl-fsSL https://get.docker.com-o get-docker.sh ...