How to Fix: CUDNN ERROR: Failed to get convolution algorithm

This error, 'CUDNN ERROR: Failed to get convolution algorithm', affects users of Keras with a source-compiled Tensorflow build, specifically when running convolution networks. It is frustrating because it prevents users from utilizing their GPU for convolution operations, leading to slower computation times and decreased model performance.

The frustration stems from the fact that cuDNN initialization appears to be the issue, yet reinstalling and recompiling TensorFlow with no results suggests there should be no conflicts with conflicting versions.

💡 Why You Are Getting This Error

• cuDNN failed to initialize properly due to a mismatch between CUDA version and the required cuDNN version. This is because cuDNN requires at least one matching version to function correctly.
• Another potential cause could be an issue with environment variables, such as incorrect paths for CUDA or CuDNN. However, appending these paths directly to the system path has already been tried without success.

🛠️ Step-by-Step Verified Fixes

💡 Conclusion

To resolve this issue, users can try updating cuDNN to its latest version and verifying compatibility with CUDA 10.0, or reinstalling TensorFlow and its dependencies. If the problem persists, it may be worth seeking further assistance from a developer or IT professional.