wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda-repo-ubuntu2204-12-2-local_12.2.2-535.104.05-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-12-2-local_12.2.2-535.104.05-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda
Error with libnvidia-extra-535 535.104.05
sudo apt --fix-broken install
export PATH=$PATH:/usr/local/cuda/bin
auto removed nvidia-firmware-535-535.86.05
https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html
sudo apt-get install zlib1g
https://developer.nvidia.com/rdp/cudnn-download
Get the tar
tar -xvf cudnn-linux-$arch-8.x.x.x_cudaX.Y-archive.tar.xz
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*
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
modified /home/fernando/.bashrc
==> For changes to take effect, close and re-open your current shell. <==
If you'd prefer that conda's base environment not be activated on startup,
set the auto_activate_base parameter to false:
conda config --set auto_activate_base false
conda create --name tf python=3.9
conda deactivate
conda activate tf
conda install -c conda-forge cudatoolkit=11.8.0
pip install nvidia-cudnn-cu11==8.6.0.163
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
pip install --upgrade pip
pip install tensorflow==2.13.*
conda install -c conda-forge jupyterlab
fish env_vars.fish
set -gx CUDNN_PATH "$CONDA_PREFIX/lib/python3.9/site-packages/nvidia/cudnn"
set -gx LD_LIBRARY_PATH "$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH"
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
