CUDA and cuDNN Compatibility Issues on NVIDIA Jetson

Issue Overview

Users of NVIDIA Jetson devices are experiencing compatibility issues between CUDA and cuDNN versions. Specifically, the problem arises when trying to install or use newer versions of CUDA (such as 11.8) on Jetson platforms. The main symptoms include:

• Inability to find a compatible cuDNN version for the installed CUDA version
• Difficulty in matching CUDA 11.8 with an appropriate cuDNN version for Jetson devices
• Uncertainty about how to revert to previous, compatible versions (e.g., CUDA 11.4 and cuDNN 8.6)

This issue impacts the user’s ability to utilize deep learning frameworks and libraries that depend on CUDA and cuDNN, potentially hindering development and deployment of AI applications on Jetson platforms.

Possible Causes

1. Version Mismatch: NVIDIA may not have released a cuDNN version compatible with CUDA 11.8 for Jetson platforms at the time of the user’s query.

2. Platform-Specific Limitations: Jetson devices might have specific hardware or software constraints that limit the versions of CUDA and cuDNN they can support.

3. Incomplete Documentation: The official NVIDIA documentation may not clearly specify which CUDA and cuDNN versions are compatible with specific Jetson models.

4. Incorrect Installation: Users might have installed a CUDA version not officially supported or recommended for their Jetson device.

5. Software Repository Issues: The required cuDNN packages might not be available or properly indexed in the software repositories used by Jetson devices.

Troubleshooting Steps, Solutions & Fixes

1. Check Compatibility Matrix:

   • Visit the NVIDIA Jetson Software Compatibility Matrix to verify supported CUDA and cuDNN versions for your specific Jetson model.
   • Ensure you are using versions that are officially supported for your device.

2. Use JetPack SDK:

   • Instead of manually installing CUDA and cuDNN, use the NVIDIA JetPack SDK, which includes pre-configured, compatible versions of CUDA and cuDNN for Jetson devices.
   • Download the appropriate JetPack version from the NVIDIA Developer website.

3. Downgrade CUDA (if necessary):
   If you need to revert to CUDA 11.4 and cuDNN 8.6, follow these steps:
   a. Remove the current CUDA installation:

   sudo apt-get --purge remove "*cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"
   

   b. Install CUDA 11.4:

   sudo apt-get update
   sudo apt-get install cuda-toolkit-11-4
   

   c. Install cuDNN 8.6 (you may need to download the appropriate .deb package from NVIDIA):

   sudo dpkg -i libcudnn8_8.6.0.xxx_arm64.deb
   

4. Update Jetson Linux:

   • Ensure your Jetson device is running the latest version of Jetson Linux, as newer versions may include updated CUDA and cuDNN packages.
   • Use the following command to update:

     sudo apt-get update && sudo apt-get upgrade
     

5. Check for Beta Releases:

   • Visit the NVIDIA Developer Forums or the official NVIDIA Developer website to check if there are any beta releases or developer previews of cuDNN that support CUDA 11.8 for Jetson platforms.

6. Use Container Solutions:

   • Consider using NVIDIA GPU Cloud (NGC) containers, which come pre-configured with compatible versions of CUDA and cuDNN for Jetson devices.
   • This approach allows you to use specific software versions without affecting the host system.

7. Consult NVIDIA Developer Support:

   • If the issue persists, reach out to NVIDIA Developer Support or post a detailed query on the NVIDIA Developer Forums, providing specific information about your Jetson model, current software versions, and the exact error messages you’re encountering.

8. Monitor for Updates:

   • Regularly check the NVIDIA Jetson download center and release notes for updates on CUDA and cuDNN compatibility for your Jetson device.

By following these steps, users should be able to resolve compatibility issues between CUDA and cuDNN on their Jetson devices, or at least identify the most appropriate versions to use for their specific hardware and software requirements.