Default Tensorflow binary does not work; outdated/incompatible cuDNN version

Commencez par localiser la définition de l’environnement conda azureml_py38_PT_TF et inspectez les versions intégrées de cuDNN et de TensorFlow. Comparez-les aux versions de la solution de contournement signalée, puis vérifiez l’environnement avec python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" ; c’est terminé lorsque l’environnement par défaut peut charger TensorFlow et détecter le GPU sans remplacer cuDNN manuellement.

The azureml_py38_PT_TF conda environment is broken and doesn't work as the current (as of 17/05/2023) as the default cuDNN binary is version 6.1, which is not compatible with any version of tensorflow.

Current work around is to manually update the cuDNN binaries as below, using the link from Nvidia

export URL="CUDNN-8.6.0-LINK-FROM-NVIDIA-WEBSITE"
# ==== DOWNLOAD CUDDN ==== 
curl $URL -o ./cudnn-linux-x86_64-8.6.0.163_cuda11-archive.tar.xz 
sudo tar -xvf ./cudnn-linux-x86_64-8.6.0.163_cuda11-archive.tar.xz
# ==== INSTALL CUDDN ==== 
sudo cp ./cudnn-*-archive/include/cudnn*.h /usr/local/cuda/include 
sudo cp -P ./cudnn-*-archive/lib/libcudnn* /usr/local/cuda/lib64 
sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
# ==== LINK UPDATED BINARIES ==== 
sudo ldconfig
# ==== INSTALL CONDA ENV ==== 
conda create -n "tfgpu" python=3.10 -y
conda activate tfgpu
conda install -c conda-forge cudatoolkit=11.8.0 ipykernel -y
python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.12.*
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
python3 -m ipykernel install --user --name tfgpu --display-name "Python (tf-cudnn8.6)"
# ==== VERIFY ==== 
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Is it possible to bump the included cuDNN version to get around this problem?

(Associated SO post)