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...
nvidia-smi -pm 1 lspci | grep NVIDIA nvidia-smi 7.3 解决:下载完 anaconda 之后仍然报错:conda: command not found 将以下添加到 Ubuntu-22.04 Linux 子系统的 /home/geoffreyfan (这里是你自己的用户名字) / 路径下的 .bashrc 文件里面: 复制 PATH=/home/user/anaconda3/bin:$PATH 在终端运行 project....
其次在WSL终端中也输入nvidia-smi,也如下图显示一样,则可以进入下一步 如果cmd中输入nvidia-smi正常输出GPU信息,而WSL中报错Command not found,参考这里: 4.2 安装CUDA 我这里安装的是CUDA 11.8.0,显卡支持cuda版本必须在11.8以上,否则需要安装更低版本的。 #先安装cuda 11.8.0 wget https://developer.download....
I found that after this setting, nvidia-smi will not longer work and generate an error message similar to the one in this discussion. However, after removing this environment variable settings. Things can go back to normal gain. A pretty irritating issue and I spent quite some time debugging...
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。
Actually, when I first set up the develop environment, everything works perfectly, after I restart the computer, the develop environment seems got problem, and nvidia-smi command output ERR! in CUDA version. I would try to install another distro. If nvidia-smi works there without error then ...
NVML (nvidia-smi) does not support all the queries yet. GPU utilization, active compute process are some queries that are not yet supported. Modifiable state features (ECC, Compute mode, Persistence mode) will not be supported. OpenGL-CUDA Interop is not yet supported. ...
检查NVIDIA Driver:在 Ubuntu on Windows 中运行nvidia-smi,会显示令人非常舒适的表格;特别注意该表格右上角显示的 CUDA Version并非安装的版本,而是 NVIDIA Driver 可最高适配的版本 检查CUDA Toolkit:在 Ubuntu on Windows 中运行nvcc --version Q5:安装好 CUDA 后,仍显示nvcc command not found,建议通过一行命令...
Use /usr/lib/wsl/lib/nvidia-smi or manually add /usr/lib/wsl/lib/ to the PATH). On multi-GPU systems it is not possible to filter for specific GPU devices by using specific index numbers to enumerate GPUs. CUDA on WSL User Guide DG-05603-001_v11.4 | 12 ...
Nvidia-WSL驱动官方文档:https://docs.nvidia.cn/cuda/wsl-user-guide/index.html 检测是否安装成功 cmd命令行输入: 如果出现类似于下图效果,即安装完成。 检测驱动是否安装完成 记下图中的CUDA version 3. 在WSL2中安装CUDA(WSL2中操作) 参考CUDA官网,选择自己对应的版本。