JetPack 6.1 Not Supporting YOLOv8, PyTorch v2.5, and TorchVision

Issue Overview

Users are experiencing compatibility issues with JetPack 6.1, specifically regarding the support for PyTorch, TorchVision, and the YOLOv8 model. The main symptoms include:

• Inability to run YOLOv8 due to unsupported versions of PyTorch and TorchVision.
• Errors arising when attempting to use Jetson-inference developed by Mr. Dusty.
• The issue occurs primarily during setup and while running specific applications that rely on these libraries.

The context involves users trying to implement machine learning models on the Nvidia Jetson Orin Nano Dev board, which is expected to support these frameworks. The frequency of the issue appears to be consistent among users attempting similar setups, significantly impacting their ability to utilize the board for AI applications.

Possible Causes

Several potential causes have been identified for the compatibility issues:

• Software Bugs or Conflicts: JetPack 6.1 may contain unresolved bugs that affect library compatibility.
• Version Mismatch: Users may be using versions of PyTorch or TorchVision that are not compatible with JetPack 6.1.
• Dependency Issues: Missing or outdated dependencies required by the libraries could lead to runtime errors.
• Configuration Errors: Incorrect installation or configuration of the libraries could prevent them from functioning correctly.
• Environmental Factors: Differences in CUDA versions or other environmental settings might contribute to the observed problems.

Troubleshooting Steps, Solutions & Fixes

To address the compatibility issues with JetPack 6.1, users can follow these troubleshooting steps:

1. Verify Installed Versions:

   • Check the installed versions of PyTorch and TorchVision using:

     python -c "import torch; print(torch.__version__)"
     python -c "import torchvision; print(torchvision.__version__)"
     

2. Install Compatible Versions:

   • If using incompatible versions, install specific versions known to work with JetPack 6.1:

     pip install torch==<compatible_version> torchvision==<compatible_version>
     

3. Use Docker Containers:

   • For a more stable environment, consider using Docker containers pre-configured for Jetson platforms:

     docker pull nvcr.io/nvidia/l4t-pytorch:r<version>
     

4. Check for Updates:

   • Regularly check for updates from Nvidia regarding JetPack, PyTorch, and other dependencies. Use the SDK Manager to ensure all components are up-to-date.

5. Test with Previous Versions:

   • If issues persist, revert to an earlier version of JetPack (e.g., JetPack 5) that is known to support the desired libraries:

     # Follow flashing instructions specific to your board
     

6. Review Documentation:

   • Consult Nvidia’s official documentation for any additional setup instructions or troubleshooting tips related to JetPack 6.1.

7. Community Support:

   • Engage with community forums for shared experiences and solutions from other users facing similar issues.

8. Log Errors:

   • When encountering errors, log them for further analysis:

     export LOGS_DIR=/path/to/logs
     # Save relevant logs here
     

9. Contact Nvidia Support:

   • If all else fails, reach out to Nvidia support with detailed logs and descriptions of the issue.

Recommended Approach

Multiple users have reported success when using Docker containers tailored for Nvidia’s Jetson platform as a workaround for library compatibility issues.

Unresolved Aspects

While various solutions have been proposed, some users continue to experience difficulties with specific configurations or setups that may require further investigation from Nvidia’s development team or community experts.