Node: 'model_3/conv1d_24/Conv1D' 
DNN library is not found. 	 [[{{node model_3/conv1d_24/Conv1D}}]] [Op:__inference_train_function_34509]

It seems that there may be compatibility issues between the versions of TensorFlow/Keras installed outside of Conda and those installed within Conda. But it maybe also cause by tensorflow 2.12 has issue, please re-install 2.10 and try again .
Specified version can work well cuDNN 8.9, CUDA 11.8 and TensorFlow 2.10.
Here is how to re-install tensorflow 2.10 instruction.

pip uninstall tensorflow
pip install tensorflow-gpu
pip install tensorflow-gpu==2.10 

ref.
TensorFlow 2.12.0 WSL2 GPU support

Have tried someother way, all of them doesn’t work when you use 2.12 tensorflow. Don’t try this. ex:

sudo cp cuda/include/cudnn*.h   /anaconda3/envs/<your environment here>/include
sudo cp cuda/lib64/libcudnn*    /anaconda3/envs/<your environment here>/lib
sudo chmod a+r /usr/local/cuda/include/cudnn*.h    /anaconda3/envs/<your environment here>/lib/libcudnn*

ref.
How to install latest cuDNN to conda?

Install

CUDA

Suppose it already install WSL2 on your system, please follow the CUDA on WSL User Guide to install WSL, for Tensorflow 2.12, suggestion CUDA version is 11.8. Can be found here CUDA Toolkit 11.8 Downloads

wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-wsl-ubuntu-11-8-local_11.8.0-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-11-8-local_11.8.0-1_amd64.deb
sudo cp /var/cuda-repo-wsl-ubuntu-11-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda

cuDNN

It still needs to install CUDNN, follow this instruction to download CUDNN 8.9.x, Installing cuDNN on Linux. Download cuDNN needs to register Nvidia account.
Download address : cuDNN Download, it has cuDNN for CUDA 12 and CUDA 11, download for CUDA 11.

Add Path to System Path

Put following seting into ~/.bashrc, thus it can update when get bash console

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

Follow tensorflow install guide to install tensorflow.

Tensorflow step by step instructions for WSL2

Python Virtual Environment

if use virtual venv, it also can work, just need to install right version tensorflow. and it needs to configure cuDNN path on activate script

vim <path to venv>/bin/activate

# After get VIRTUAL_ENV, include the CUDNN path
export VIRTUAL_ENV

CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$VIRTUAL_ENV/lib/:$CUDNN_PATH/lib

Reference

WSL2 安裝 CUDA Toolkit、cuDNN

The post Run Keras or TensorFlow in WSL2 with Nvidia GPU appeared first on richliu's blog.

主機版是 Z97-A-USB31
CPU 是 i7-5775C 內建的 GPU

現在看起來在 GRUB 開機時加上參數 nomodeset 就可以了.

網路上找來對 nomodeset 的說明

The newest kernels have moved the video mode setting into the kernel. 
So all the programming of the hardware specific clock rates and 
registers on the video card happen in the kernel rather than in 
the X driver when the X server starts.. This makes it possible 
to have high resolution nice looking splash (boot) screens and 
flicker free transitions from boot splash to login screen. 
Unfortunately, on some cards this doesn't work properly and you 
end up with a black screen. Adding the nomodeset parameter 
instructs the kernel to not load video drivers and use BIOS
 modes instead until X is loaded.

也就是說 modeset 已經搬到 kernel 了. 但是有時在 kernel 的 modeset 會有問題, 這就是告訴 kernel 用 grub 的設定, 直到 X 啟動

ref.
https://askubuntu.com/questions/716957/what-do-the-nomodeset-quiet-and-splash-kernel-parameters-mean/716966

The post GRUB 開機會停在 Load Ramdisk 的問題 appeared first on richliu's blog.