The runtimeerror: cudnn error: cudnn_status_not_initialized error occurs if there is an issue with the CUDA Deep Neural Network (cuDNN) library.
This error shows that the cuDNN library is unable to initialize the required resources. The error message usually looks like this:
RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED
This error typically occurs during the initialization of the cuDNN library, or while running a deep learning model that uses cuDNN.
Possible Common Causes of the Error
There are various possible causes of the cudnn error: cudnn_status_not_initialized and the following are:
- Incompatible cuDNN version
- Missing or corrupt cuDNN files
- Insufficient GPU memory
- Out-of-date GPU drivers
How to Fix the Error?
Here are some solutions to resolve this error:
Solution 1: Check cuDNN version
Make sure that you’re using a compatible version of the cuDNN library with your deep learning framework. If you are not sure, check the compatibility chart on the NVIDIA website.
Solution 2: Reinstall cuDNN
If the required cuDNN files are missing or corrupted, you need to try to reinstall the cuDNN library.
Solution 3: Check GPU memory
If your GPU doesn’t have enough memory to initialize the cuDNN library, you can attempt to reduce the batch size or use a GPU with higher memory.
Also, you can try optimizing the model to reduce the memory usage.
Solution 4: Update GPU drivers
Ensure to update your GPU drivers to the latest version. The out-of-date GPU drivers can cause compatibility problems with the cuDNN library.
Solution 5: Check GPU compatibility
Check if your GPU is compatible with the cuDNN library. Some older GPUs might not be compatible with the latest version of the cuDNN library.
Solution 6: Verify the installation
Ensure that the cuDNN library is installed correctly. Check if the required files are in the correct directories.
Solution 7: Restart the system
Restarting the system can sometimes resolve the cudnn error: cudnn_status_not_initialized error.
Note: Remember to keep your cuDNN library, deep learning framework, and GPU drivers up to date to avoid compatibility issues that can lead to this error.
More Resources
The following articles can help you to understand more about CUDA ERROR:
- Runtimeerror: cuda error: invalid device ordinal
- Runtimeerror: couldnt install torch
- Runtimeerror: cuda error: device-side assert triggered
Conclusion
The runtimeerror: cudnn error: cudnn_status_not_initialized error is a common problem which occurs while training deep learning models.
In this article, we discussed the possible causes of the error, and provide solutions to resolve the error.
If you encounter this error, you may try the solutions in this article. If none of the solutions work, seek help from the community or the expert programmer.
FAQs
cuDNN is a library that provides GPU-accelerated operation of deep learning to improve the performance of deep learning frameworks such as TensorFlow and PyTorch.
Can insufficient GPU memory cause the cudnn error: cudnn_status_not_initialized error?
Yes, if your GPU doesn’t have enough memory to initialize the cuDNN library, it can cause this error. Try reducing the batch size or use a GPU with higher memory.
You can update your GPU drivers through the device manager or by downloading the latest drivers from the GPU manufacturer’s website.
If reinstalling the cuDNN library doesn’t resolve the error, try checking the compatibility of the cuDNN library with your deep learning framework or updating your GPU drivers.
