Jetson Orin Nano Dev Board: Issues Installing PyTorch 2.1.0

Issue Overview

Users of the Nvidia Jetson Orin Nano Dev Board are encountering difficulties when attempting to install PyTorch 2.1.0 while using JetPack 5.1.2 and Python 3.8. The primary symptom reported is that instead of the desired version (2.1.0), an older version (1.8.0) is being installed, which leads to subsequent problems in application functionality.

This issue arises during the installation process of PyTorch and has been consistently observed by multiple users. The impact on user experience is significant, as the incorrect version creates compatibility issues with other packages, particularly when attempting to run applications that depend on specific features or functionalities available only in newer versions of PyTorch.

Possible Causes

• Dependency Conflicts: The installed version of PyTorch (1.8.0) may have dependencies that conflict with the desired version (2.1.0), particularly relating to cuBLAS.

• Incorrect Package Management: Users may inadvertently have package management issues, such as remnants of previous installations affecting new installs.

• Version Mismatch: The specific version of JetPack or Python being used might not fully support the installation of PyTorch 2.1.0, leading to fallback to an older version.

• Installation Command Errors: Users may not be using the correct commands or package sources for installing the desired version of PyTorch.

Troubleshooting Steps, Solutions & Fixes

1. Verify Current PyTorch Version:

   • Open a terminal and run:

     python3
     

   • Then enter:

     import torch
     print(torch.__version__)
     

   • This will confirm which version is currently installed.

2. Remove Existing PyTorch Installation:

   • If the output indicates that version 1.8.0 is installed, remove it using:

     pip uninstall torch torchvision
     

3. Install Correct Version of PyTorch:

   • Download the appropriate wheel file for PyTorch 2.1.0:

     wget <URL_to_torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl>
     

   • Replace <URL_to_torch> with the actual URL where the wheel file can be downloaded.

4. Verify Checksum:

   • After downloading, verify the checksum to ensure integrity:

     md5sum torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl
     

   • Ensure the output matches 0063b1373e339e47995afd093a3cde87.

5. Install the Downloaded Wheel:

   • Run the following command to install the desired version:

     pip install torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl
     

6. Install Additional Packages:

   • If you need to install Ultralytics YOLOv8 without affecting your current installations, use:

     pip install ultralytics
     

   • Monitor for any errors during this installation.

7. Best Practices for Future Installations:

   • Always check compatibility between JetPack versions and package requirements before installation.
   • Use virtual environments to manage dependencies effectively and avoid conflicts.
   • Regularly update pip and setuptools to their latest versions using:

     pip install --upgrade pip setuptools
     

By following these steps, users should be able to resolve the installation issues with PyTorch on their Nvidia Jetson Orin Nano Dev Board effectively, ensuring a smoother development experience moving forward.