Unable to Import TensorRT in Python with Jetpack 6.0DP

Issue Overview

Users are experiencing difficulties importing the TensorRT module in Python when using NVIDIA Jetpack 6.0 Developer Preview (DP). The specific error message encountered is "No module named ‘tensorrt’" when attempting to import TensorRT in a Python environment. This issue occurs despite TensorRT being installed on the system, as confirmed by the package manager. The problem appears to be related to the Python environment not recognizing the installed TensorRT package, potentially due to incompatibilities between the installed version and the Python package management system.

Possible Causes

1. Package Installation Method: The TensorRT package (version 8.6.2.3-1+cuda12.2) is installed via the system package manager (dpkg) rather than through pip, which may cause Python to not recognize it as an importable module.

2. Path Configuration: The Python environment may not be correctly configured to find the TensorRT module, possibly due to missing or incorrect PATH or PYTHONPATH settings.

3. Version Incompatibility: The installed TensorRT version might not be compatible with the current Python version or the Jetpack 6.0DP environment.

4. Incomplete Installation: The TensorRT installation might be incomplete, missing necessary Python bindings or configuration files.

5. Environment Isolation: If using virtual environments, the TensorRT module might not be accessible within the active environment.

Troubleshooting Steps, Solutions & Fixes

1. Verify TensorRT Installation:
   Confirm the TensorRT installation using the following command:

   sudo dpkg -l | grep tensorrt
   

   This should show the installed TensorRT package and version.

2. Check Python Path:
   Ensure that the TensorRT Python package is in the Python path. You can check the current Python path with:

   import sys
   print(sys.path)
   

   Look for directories related to TensorRT in the output.

3. Locate TensorRT Python Package:
   Find the TensorRT Python package on your system:

   sudo find / -name tensorrt
   

   Note the location of any tensorrt directories found.

4. Add TensorRT to PYTHONPATH:
   If the TensorRT Python package is not in the Python path, add it:

   export PYTHONPATH=$PYTHONPATH:/path/to/tensorrt
   

   Replace /path/to/tensorrt with the actual path found in step 3.

5. Install TensorRT Python Wheel:
   If available, try installing the TensorRT Python wheel:

   pip3 install /usr/lib/python3.8/dist-packages/tensorrt-8.6.2-cp38-none-linux_aarch64.whl
   

   Adjust the path and filename according to your specific TensorRT version and Python version.

6. Create Symbolic Links:
   If the TensorRT Python package is installed but not recognized, create symbolic links:

   sudo ln -s /usr/lib/python3.8/dist-packages/tensorrt /usr/local/lib/python3.8/dist-packages/tensorrt
   

   Adjust the paths according to your Python version and installation locations.

7. Rebuild TensorRT Python Bindings:
   If the above steps don’t work, you may need to rebuild the TensorRT Python bindings:

   cd /usr/src/tensorrt/samples/python
   sudo python3 setup.py install
   

8. Check for Conflicting Packages:
   Ensure there are no conflicting TensorRT or CUDA packages installed via pip:

   pip3 list | grep tensorrt
   pip3 list | grep cuda
   

   If found, consider removing them to avoid conflicts with the system-installed versions.

9. Verify CUDA Configuration:
   Ensure that CUDA is properly configured, as TensorRT depends on it:

   nvcc --version
   

   This should display the CUDA version information.

10. Consult NVIDIA Documentation:
    Refer to the official NVIDIA documentation for Jetpack 6.0DP and TensorRT for any specific installation or configuration requirements.

11. Contact NVIDIA Support:
    If the issue persists after trying these solutions, consider reaching out to NVIDIA support or posting on their official developer forums for more specific assistance.

Remember to restart your Python environment or shell session after making any changes to environment variables or system configurations. If using virtual environments, ensure that they are properly activated and configured to access system-wide packages if necessary.