RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED I have tried to reinstall WSL, CuDNN and recreated the conda environment, but the problem still persists.
4. 如果有之前的残留版本最好卸载干净; sudorm-rf /usr/local/cuda/include/cudnn.hsudorm-rf /usr/local/cuda/lib64/libcudnn* 5. 查看cudnn的版本; cat/usr/local/cuda/include/cudnn.h |grepCUDNN_MAJOR -A2 注意,可能以上脚本不能直接查看cudnn版本,需要查看cudnn_version.h文件获取; 参考: 1.n...
My answer shows how to check the version of CuDNN installed, which is usually something that you also want to verify. You first need to find the installed cudnn file and then parse this file. To find the file, you can use: whereis cudnn.h CUDNN_H_PATH=$(whereis cudnn.h) If ...
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2 1. 注意,可能以上脚本不能直接查看cudnn版本,需要查看cudnn_version.h文件获取; 参考: 1. nvidia_install_cudnn_linux; 完
I could checkCUDA_VERSIONin host by : cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 but by running this command in docker container: cat: /usr/include/cudnn_version.h: No such file or directory and by checkin...
To install the CUDA Toolkit on Ubuntu 24.04, 22.04, or 20.04, you can use NVIDIA’s official APT repository mirror. This method ensures that you have access to the latest version of the toolkit, along with any updates or patches released by NVIDIA. This guide will walk you through the ins...
Aside, pip install auto-gptq fails to compile the CUDA extension here as well, returns an error: running build_ext /home/user/Envs/text-generation-webui_env/lib/python3.10/site-packages/torch/utils/cpp_extension.py:399: UserWarning: There are no x86_64-linux-gnu-g++ version bounds defined...
path-to-resource:version-number 应以其他方式指定环境的“最新”版本 path-to-resource@latest 映像生成问题 ACR 问题 ACR 不可访问 当访问工作区的关联 Azure 容器注册表 (ACR) 资源失败时,可能会发生此问题。 可能的原因: 工作区的 ACR 位于虚拟网络 (VNet)(专用终结点或服务终结点)后面,并且你没有使用...
安装cuDNN v7.6.4 for CUDA 10.1。 不能安装多个版本的 cuDNN。 下载 cuDNN v7.6.4 zip 文件并将其解压缩后,将<CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll复制到<YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin。
I have tried to run the following script to check if tensorflow can access the GPU or not. python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" The script raises the following error when run - could not load dynamic library 'libcudnn.so.8'; ...