Fix CUDA_ERROR_LAUNCH_FAILED and CUDNN_STATUS_INTERNAL_ERROR in TensorFlow

When encountering the "CUDA_ERROR_LAUNCH_FAILED: unspecified launch failure" and "CUDNN_STATUS_INTERNAL_ERROR" errors in TensorFlow, it usually indicates a problem with the execution of CUDA operations or an internal error in the cuDNN library.

Here's a detailed explanation of the troubleshooting steps along with examples:

1. Check CUDA and GPU driver compatibility:

   • Ensure that you have installed a compatible version of CUDA for your GPU. Check the TensorFlow documentation for the recommended CUDA version.
   • Verify that you have the appropriate GPU driver installed for your CUDA version.
   • Example: If your GPU is compatible with CUDA 10.1, make sure you have CUDA 10.1 installed and the corresponding GPU driver.

2. Verify GPU availability:

   • Check if the GPU is recognized and available in your system.
   • Ensure that the GPU is not being used by another process.
   • Example: Run the command nvidia-smi to check if the GPU is visible and available.

3. Update TensorFlow and CUDA:

   • Update TensorFlow and CUDA to the latest stable versions. Newer releases often come with bug fixes and improvements.
   • Example: Use the appropriate package manager (pip, conda) to update TensorFlow and CUDA: pip install --upgrade tensorflow-gpu or conda install tensorflow-gpu.

4. Check GPU memory:

   • Insufficient GPU memory can cause launch failures. Make sure your model and data fit within the available GPU memory.
   • Reduce the batch size or modify your code to minimize GPU memory usage.
   • Example: Decrease the batch size in your training code: batch_size = 32.

5. Restart the runtime/environment:

   • Sometimes, temporary issues can cause launch failures. Restart the runtime, reset the environment, or reboot your machine.
   • Example: Restart your Jupyter Notebook kernel or IDE.

6. Verify code and model:

   • Review your code and model architecture for any issues.
   • Check for any custom CUDA operations or GPU-specific code that might be implemented incorrectly.
   • Example: Check if you are using any unsupported operations or if there are any errors in your CUDA code.

7. Seek community support:

   • If the above steps don't resolve the issue, seek help from the TensorFlow community or the NVIDIA developer forums.
   • Provide detailed information about your system setup, TensorFlow version, CUDA version, and any relevant code snippets or error logs.
   • Example: Post your issue on the TensorFlow GitHub repository or relevant forums, including all relevant details and code snippets.

By following these troubleshooting steps, you can address the "CUDA_ERROR_LAUNCH_FAILED: unspecified launch failure" and "CUDNN_STATUS_INTERNAL_ERROR" errors in TensorFlow and improve the chances of resolving the issue.

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Still stuck? This error is one of four common causes of TensorFlow GPU library failures. Work through the CUDA and cuDNN library load error diagnostic guide to identify which one applies to you.