Using TensorRT in a Conda Environment on Jetson Orin Nano

Issue Overview

Users of the Nvidia Jetson Orin Nano 8GB are experiencing difficulties using TensorRT (TRT) and PyCuda packages within a Miniconda environment. While these packages are pre-installed and available for use after flashing the Jetpack, they are not discoverable when running programs from within a Conda environment. This issue impacts the ability to utilize TRT and PyCuda in custom Python environments, potentially hindering development workflows and project-specific configurations.

Possible Causes

1. Environment Isolation: Conda environments are designed to be isolated from the system-wide Python installation, which may prevent access to globally installed packages.

2. Path Configuration: The Conda environment may not be configured to include the paths where TensorRT and PyCuda are installed on the Jetson system.

3. Python Version Mismatch: If the Conda environment uses a different Python version than the system-wide installation, it may not be compatible with the pre-installed TensorRT and PyCuda packages.

4. Package Management Conflict: Conda’s package management system might not be aware of the system-wide installations, leading to discoverability issues.

Troubleshooting Steps, Solutions & Fixes

1. Use System Site Packages
   The most straightforward solution is to create a Conda environment that has access to the system-wide packages. This can be achieved by using the --system-site-packages option when creating the environment.

   To create a new Conda environment with access to system packages:

   conda create --name myenv --system-site-packages
   

   To activate the environment:

   conda activate myenv
   

2. Verify Python Version Compatibility
   Ensure that the Python version in your Conda environment matches the system Python version used by the pre-installed TensorRT and PyCuda packages.

   To check the Python version in your Conda environment:

   python --version
   

   Compare this with the system Python version:

   /usr/bin/python3 --version
   

3. Manual Package Installation
   If the above solutions don’t work, you may need to manually install TensorRT and PyCuda in your Conda environment. However, this should be a last resort as it may lead to version conflicts.

   For TensorRT:

   conda install -c nvidia tensorrt
   

   For PyCuda:

   conda install -c conda-forge pycuda
   

4. Environment Variable Configuration
   If the packages are still not discoverable, you may need to set environment variables to point to the correct locations:

   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
   export PYTHONPATH=$PYTHONPATH:/usr/lib/python3.x/dist-packages
   

   Replace x with the appropriate Python version number.

5. Create a Symlink
   As an alternative to using --system-site-packages, you can create symlinks in your Conda environment to the system-wide packages:

   ln -s /usr/lib/python3.x/dist-packages/tensorrt /path/to/conda/env/lib/python3.x/site-packages/
   ln -s /usr/lib/python3.x/dist-packages/pycuda /path/to/conda/env/lib/python3.x/site-packages/
   

   Replace /path/to/conda/env with the actual path to your Conda environment and x with the appropriate Python version number.

6. Check for Conda Forge Versions
   If the above solutions don’t work, check if compatible versions of TensorRT and PyCuda are available through the Conda Forge channel:

   conda search -c conda-forge tensorrt
   conda search -c conda-forge pycuda
   

   If suitable versions are found, you can install them in your Conda environment:

   conda install -c conda-forge tensorrt pycuda
   

Remember to test your setup after implementing any of these solutions to ensure that TensorRT and PyCuda are now discoverable and functioning correctly within your Conda environment.