Update-alternatives: using /usr/local/cuda-11. linux-ppc64le v12.1.0 linux-64 v12.1.0 linux-aarch64 v12.1.0 win-64 v12.1.0 conda install To install this package run one of the following: conda install -c. Press to keep the current choice, or type selection number: 1 Be aware that older versions of CUDA (<10) don’t support WSL 2. There are 2 choices for the alternative cuda (providing /usr/local/cuda). usr/local/cuda-11.5 - priority 115 /usr/local/cuda-11.6 - priority 116Īnd To make the CUDA-11.5 is the active version # update-alternatives -config cuda The tensorflow package supports GPU accelerated operations via Nvidia CUDA. Link currently points to /usr/local/cuda-11.6 Link best version is /usr/local/cuda-11.6 So we will install the previous CUDA Version # apt install cuda-11-5Īnd to display all CUDA Alternatives # update-alternatives -display cuda To confirm whether CUDA is working, reboot the system, then run the following command:īut what if we need to install CUDA 11.5, for example, need to run the CUDA 11.5 based cuDNN library libcudnn8 8.3.3.40-1+cuda11.5 If (you found your GPU Card is CUDA Supported) ' > ~/.bashrc All you need to install yourself is the latest nvidia-driver (so that it works with the latest CUDA level and all older CUDA levels you use.) This has many advantages over the pip install tensorflow-gpu method: Anaconda will always install the CUDA and CuDNN version that the TensorFlow code was compiled to use. Check your GPU Graphic Card for the CUDA Enabled feature, the compute capability listed here for Nvidia CUDA GPUs Graphic Cards.
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