Solve the RuntimeError: cuDNN error: cuDNN_STATUS_NOT_INITIALIZED

The error message, RuntimeError: cuDNN error: cuDNN_STATUS_NOT_INITIALIZED, is related to the cuDNNA library for accelerating deep neural network operations. library, which is used for deep learning operations on NVIDIA GPUs. This error typically occurs when cuDNN is not initialized properly or encounters an issue during initialization. However, there are some ways to resolve this issue.

Solution

Let’s discuss a few potential solutions to rectify this error.

Checking CUDA and cuDNN installation

The cuDNN library is typically used in conjunction with the CUDA toolkit. By ensuring that we have CUDA and cuDNN installed correctly, in addition to properly setting the CUDA environment variablesThese are special variables that can be set in our system's environment to control various aspects of the CUDA runtime and behavior., we can prevent this error from occurring.

Here is the code to check if CUDA and cuDNN are installed properly:

import torch
if not torch.cuda.is_available():
    print("CUDA driver is not installed.")
else:
    print("CUDA driver is installed.")
if torch.backends.cudnn.is_available():
    print("cuDNN is installed.")
else:
    print("cuDNN is not installed.")

Note: If we have already installed CUDA and cuDNN, we can uninstall and reinstall cuDNN and CUDA to ensure a clean installation.

We can refer to NVIDIA’s documentation for the installation of CUDA and cuDNN to follow the installation instructions for the specific platform.

Ensuring libraries’ compatibility

When using PyTorch or TensorFlow, we must ensure that we have the compatible versions installed. The following Python code verifies the installed versions of PyTorch or TensorFlow:

#importing the pytorch and tensorflow libraries
import torch
import tensorflow as tf
#printing the versions of tensorflow and pytorch 
print(tf.__version__)
print(torch.__version__)

Sometimes, updating or downgrading these libraries can help resolve compatibility issues with cuDNN.

Checking GPU drivers and their memory

In addition, we can make sure we have the latest NVIDIA GPU drivers installed on our systems. The code to do this is given below:

import torch
# Check GPU Drivers and CUDA Version
def check_gpu_driver():
    if torch.cuda.is_available():
        current_device = torch.cuda.current_device()
        driver_version = torch.cuda.getDriverVersion()
        cuda_version = torch.version.cuda
        print(f"GPU Driver Version: {driver_version}")
        print(f"CUDA Version: {cuda_version}")
    else:
        print("No GPU available.")
if __name__ == "__main__":
    check_gpu_driver()

After installing the latest NVIDIA GPU drivers, cuDNN errors can still arise due to the GPU running out of memory when using it in our coding projects. Therefore, we must ensure our GPU has enough memory to perform the operations we are trying to run, as shown below:

import torch
# Check GPU memory i.e how much memory is there, how much is free
def check_gpu_memory():
    if torch.cuda.is_available():
        current_device = torch.cuda.current_device()
        gpu = torch.cuda.get_device_properties(current_device)
        print(f"GPU Name: {gpu.name}")
        print(f"GPU Memory Total: {gpu.total_memory / 1024**2} MB")
        print(f"GPU Memory Free: {torch.cuda.memory_allocated(current_device) / 1024**2} MB")
        print(f"GPU Memory Used: {torch.cuda.memory_reserved(current_device) / 1024**2} MB")
    else:
        print("No GPU available.")
if __name__ == "__main__":
    check_gpu_memory()

Recheck the application code

When running a particular deep learning application, such as a neural network training code, we must ensure that our code initializes the GPU and cuDNN correctly by eliminating any code issuesSuch issues include incorrectly initializing the GPU, using incorrect tensor shapes and sizes, and mixing up GPU and CPU operations by not managing the device context correctly..

Here is the code for correctly configuring the GPU:

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)  # Move your model to the selected device
Configuring the GPU

cuDNN is normally initialized automatically alongside importing the torch library. However, if we wish to initialize cuDNN normally, we can do this by the following code:

torch.backends.cudnn.enabled = True  # Enable cuDNN
torch.backends.cudnn.benchmark = True  # Use cuDNN's auto-tuner for the best performance
Initializing cuDNN

Note: If you want to learn more about CUDA runtime errors, you can visit this answer.

Conclusion

If the above methods don’t resolve the error, we can resort to basic methods such as rebooting our system and checking for any physical damage or overheating issues with our GPU. It’s important to know that before trying any of the methods above, we must always remember to make backups before making significant changes to our system, especially when dealing with GPU drivers and libraries.

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