TensorFlow with GPU Support in JetPack SDK

Issue Overview

Users are experiencing difficulties in setting up TensorFlow with GPU support on the Nvidia Jetson Orin Nano Dev board using JetPack SDK 6.0. The specific symptoms include:

• Inability to find prebuilt versions of TensorFlow 2.17.0 compatible with JetPack 6.0.
• Confusion regarding the default Python version, which is 3.10 due to the use of Ubuntu 22.04 in JetPack 6.
• The default OpenCV provided with JetPack does not support CUDA, leading users to seek guidance on building OpenCV from source.

The problem typically arises during the setup phase when users attempt to install and configure the necessary libraries for their projects. The issue is consistent among users attempting to run projects that require GPU acceleration for TensorFlow and OpenCV. This situation significantly impacts user experience as it hinders the ability to leverage the full capabilities of the Jetson Orin Nano for machine learning and computer vision tasks.

Possible Causes

Several potential causes have been identified for the issues described:

• Hardware Incompatibilities or Defects: While less likely, any hardware issues with the Jetson Orin Nano could lead to problems during library installation or execution.

• Software Bugs or Conflicts: The absence of prebuilt binaries for TensorFlow 2.17.0 may indicate compatibility issues or delays in support for newer versions.

• Configuration Errors: Incorrect configurations during library installation could lead to failures in enabling GPU support.

• Driver Issues: Outdated or incompatible drivers may prevent proper communication between TensorFlow, OpenCV, and the GPU.

• User Errors or Misconfigurations: Users may not be following the correct procedures for installation, particularly when it comes to building libraries from source.

Troubleshooting Steps, Solutions & Fixes

To resolve these issues, users can follow these comprehensive troubleshooting steps and solutions:

1. Verify Python Version:

   • Ensure that Python 3.10 is being used by running:

     python3 --version
     

2. Install Compatible TensorFlow Version:

   • Since TensorFlow 2.17.0 is not available, consider using TensorFlow 2.16.0 instead:
     • Download from NVIDIA’s official site.
   • Follow the installation instructions provided in the NVIDIA documentation titled "Installing TensorFlow for Jetson Platform".

3. Build OpenCV from Source with CUDA Support:

   • The default OpenCV does not support CUDA; therefore, it needs to be built from source:
     • First, remove any existing OpenCV installations:

       sudo apt -y purge *libopencv*
       

     • Use the following script to build OpenCV 4.9.0:

       #!/bin/bash
       
       version="4.9.0"
       folder="workspace"
       
       set -e
       
       for (( ; ; ))
       do
           echo "Do you want to remove the default OpenCV (yes/no)?"
           read rm_old
       
           if [ "$rm_old" = "yes" ]; then
               echo "** Remove other OpenCV first"
               sudo apt -y purge *libopencv*
               break
           elif [ "$rm_old" = "no" ]; then
               break
           fi
       done
       

     • Follow additional instructions from the script found at AastaNV’s GitHub repository.

4. Check Driver Installation:

   • Ensure that all necessary drivers are installed and up-to-date by running:

     sudo apt update && sudo apt upgrade
     

5. Testing and Validation:

   • After installation, validate that TensorFlow can access the GPU by running a simple test script:

     import tensorflow as tf
     print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
     

6. Best Practices for Future Installations:

   • Always check for compatibility of libraries with your specific version of JetPack.
   • Regularly update your system and libraries to avoid conflicts.
   • Refer to official NVIDIA documentation for any specific configurations required for new library versions.

By following these steps, users should be able to successfully set up their environment for using TensorFlow with GPU support on the Nvidia Jetson Orin Nano Dev board using JetPack SDK 6.0. If issues persist, further investigation into specific error messages or logs may be necessary to identify unresolved aspects of the problem.