Unable to install torch_tensorrt for Jetpack 6.0

Issue Overview

Users are experiencing difficulties when attempting to install the torch_tensorrt package for JetPack 6.0 on the Nvidia Jetson Orin Nano Dev board. The primary symptoms include error messages indicating that a compatible version of the torch library cannot be found, specifically the requirement for torch versions between 2.4.0 and 2.5.0. The installation process fails during the execution of pip, resulting in a "No matching distribution found" error.

This issue arises during the installation phase, particularly when users are trying to set up their development environment for machine learning applications using PyTorch and TensorRT. The hardware in question is the Nvidia Jetson Orin Nano, and the software environment includes JetPack 6.0, which is a software development kit (SDK) designed for AI applications.

The problem appears to be consistent among multiple users, impacting their ability to utilize Torch-TensorRT effectively, thus hindering their development workflow.

Possible Causes

• Software Bugs or Conflicts: The current version of torch_tensorrt may not be fully compatible with JetPack 6.0, leading to installation failures.

• Configuration Errors: Users may not have set up their environment correctly or may lack necessary configuration files (e.g., WORKSPACE file) that are required for building projects with Bazel.

• Driver Issues: Incompatibilities between installed drivers and the required versions of libraries can lead to installation problems.

• Environmental Factors: Issues such as insufficient system resources or incorrect Python environment settings (e.g., virtual environments) could also contribute to these errors.

• User Errors or Misconfigurations: Users might not be following the correct installation procedures or may be using outdated commands that do not align with the requirements of JetPack 6.0.

Troubleshooting Steps, Solutions & Fixes

1. Verify Compatibility:

   • Ensure that you are using a compatible version of PyTorch with torch_tensorrt. For JetPack 6.0, confirm that you have installed PyTorch version 2.4.x.
   • Check for updates on the official NVIDIA forums or GitHub repositories regarding compatibility with JetPack 6.0.

2. Install Using Correct Command:

   • Use the following command to install torch_tensorrt directly from the NVIDIA repository:

     pip install torch-tensorrt -f https://github.com/NVIDIA/Torch-TensorRT/releases
     

3. Adjust Setup Configuration:

   • If you encounter issues during installation, consider modifying your setup.py file to accommodate JetPack 6.0:

     python setup.py bdist_wheel --jetpack-version 6.0 --use-cxx11-abi
     

   • Ensure that you have created or updated the WORKSPACE file for JetPack 6.0 as follows:

     workspace(name = "Torch-TensorRT")
     
     load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")
     load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
     
     http_archive(
         name = "rules_python",
         sha256 = "863ba0fa944319f7e3d695711427d9ad80ba92c6edd0b7c7443b84e904689539",
         strip_prefix = "rules_python-0.22.0",
         url = "https://github.com/bazelbuild/rules_python/releases/download/0.22.0/rules_python-0.22.0.tar.gz",
     )
     
     load("@rules_python//python:repositories.bzl", "py_repositories")
     
     py_repositories()
     

4. Check Python Environment:

   • Ensure that you are operating within a correctly configured virtual environment (venv). Activate your venv before running installation commands:

     source /path/to/your/venv/bin/activate
     

5. Isolate Issues:

   • Test installations in a clean environment to rule out conflicts with other packages.
   • If possible, try using a different version of JetPack or downgrade to an earlier version (like JetPack 5.x) where compatibility is confirmed.

6. Consult Documentation and Community Resources:

   • Refer to official NVIDIA documentation for any updates on Torch-TensorRT and its compatibility with different versions of JetPack.
   • Engage with community forums for additional support and shared experiences related to similar issues.

7. Best Practices:

   • Regularly check for updates on both PyTorch and TensorRT libraries.
   • Maintain backups of your configurations and environments to easily revert changes if needed.

By following these troubleshooting steps, users should be able to address installation issues related to torch_tensorrt on their Nvidia Jetson Orin Nano Dev board running JetPack 6.0 effectively.