Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.1.0

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I get this error when I run the model.fit_generator code to train images using the CNN model. I don't understand the error, and what should I do? Can anyone help me? this is the full error description `Loaded runtime CuDNN library: 8.0.5, but the source was compiled with: 8.1.0. CuDNN library needs to have a matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, ensure the library loaded at runtime is compatible with the version specified during compile configuration.

tensorflow
4 Answers

I had the same error "tensorflow/stream_executor/cuda/cuda_dnn.cc:362] Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.1.0." I solved it by downgrading the TensorFlow version, here it says that you use a new version of TensorFlow that is not compatible with the google colab CuDNN version. I used TensorFlow 2.4.0 plus all the dependence required on version 2.4.0.
Here it says which version of TensorFlow to use for cudnn compatibility, https://www.tensorflow.org/install/source

user_146025460

You should always have version of libraries installed that is matching the version dependency you want to use is compiled with.

You can download the version you need from nvidia website or use conda for package management. It will handle all dependencies for you.

You can miniconda and type conda install -c anaconda tensorflow-gpu to get it sorted for you. If you need a specific version of python, you can create environment with it.

user_60846170

My solution:

After confirming that my cuda and cudnn versions are compatible with tensorflow, I first thought that the system did not synchronize after the installation was completed. After several restarts, it was found that it was not and could not be the problem, so I started to check all the cuda in the system. For the software that depends on cudnn, matlab was uninstalled during the period but it was useless. Later, I thought that pytorch is also related to cuda and cudnn. I checked the version of pytorch and found that I was using torch 1.8, and the cuda it was adapted to was 11.1 , The corresponding cudnn is 8.0.5, now the case is solved. Finally upgraded pytorch and solved it.

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