Kernel Crash in Jupyter Notebook with WhisperX on Kubeflow

Based on the information provided, it appears that you're experiencing kernel crashes in your Jupyter Notebook when trying to run WhisperX for audio transcription on Kubeflow. Let's address your questions:

1. Potential Cause: Yes, the kernel crash is likely related to the missing library libcudnn_ops_infer.so.8. This library is part of the NVIDIA cuDNN (CUDA Deep Neural Network) package, which is essential for GPU-accelerated operations in deep learning frameworks. The warning message you're seeing in the terminal is indicative of this missing dependency.

2. Resolution: To resolve this issue and ensure the necessary CUDA libraries are available in your Jupyter Notebook environment on Kubeflow, you can try the following steps:

a) Verify CUDA and cuDNN installation: Make sure that both CUDA and cuDNN are properly installed and configured in your Kubeflow environment. The versions should be compatible with your CUDA 12.4 installation.

b) Update environment variables: Ensure that the LD_LIBRARY_PATH environment variable includes the path to the cuDNN libraries. You may need to add this to your Jupyter kernel specification or Kubeflow configuration.

c) Install cuDNN in your Jupyter environment: If possible, try installing cuDNN within your Jupyter environment. This might require administrative access to your Kubeflow cluster.

d) Use a pre-configured environment: Consider using a pre-configured Kubeflow environment that includes all necessary CUDA and deep learning libraries.

e) Rebuild your Jupyter kernel: Create a new Jupyter kernel with all the required dependencies, including the correct versions of CUDA, cuDNN, and other necessary libraries.

3. Alternative Approaches: If GPU acceleration continues to be problematic, you can consider the following alternatives:

a) CPU-only mode: Configure WhisperX to run in CPU-only mode. This will be slower but may avoid the CUDA-related issues. You can typically do this by setting an environment variable like CUDA_VISIBLE_DEVICES="" before running your code.

b) Use a different transcription library: Consider using an alternative transcription library that may have fewer GPU dependencies or better compatibility with your current environment.

c) Containerized approach: Use a Docker container with all necessary dependencies pre-installed, which you can then run within your Kubeflow environment.

d) Cloud-based transcription services: If feasible for your use case, consider using cloud-based transcription services that don't require local GPU resources.

Remember to always ensure you have the necessary permissions and follow your organization's guidelines when making changes to your Kubeflow environment. If the issue persists after trying these solutions, you may need to consult with your Kubeflow administrator or seek support from the WhisperX community for more specific guidance.

Sources
SageMake jupyterlab kernel dies | AWS re:Post
Using self-hosted Jupyter notebooks - Amazon EMR