Libopenblas.so.0 not found

Issue Overview

Users are encountering an ImportError while attempting to run PyTorch on the Nvidia Jetson Orin Nano, specifically the error message:

ImportError: libopenblas.so.0: cannot open shared object file: No such file or directory

This issue arises during the execution of PyTorch applications, indicating that the system is unable to locate the libopenblas.so.0 library, which is crucial for numerical computations in PyTorch. The problem persists even after users have attempted to install the libopenblas package. The frequency of this issue appears to be consistent among users who have installed PyTorch on their Jetson Orin Nano boards, particularly those running JetPack 5.1.

The impact of this problem is significant as it prevents users from utilizing PyTorch for machine learning tasks, thereby hindering development and experimentation on the Jetson platform.

Possible Causes

1. Missing Library: The libopenblas.so.0 library may not be installed or properly linked in the system.

   • This can lead to the observed ImportError since PyTorch relies on this library for its operations.

2. Configuration Errors: Incorrect library paths in environment variables (like LD_LIBRARY_PATH) could prevent the system from locating libopenblas.so.0.

   • Misconfigured paths can cause runtime errors when dependent libraries are not found.

3. Driver Issues: Incompatibilities between installed drivers and the library versions could lead to failures in loading necessary components.

   • Driver mismatches can disrupt library functionality and lead to unresolved dependencies.

4. User Errors: Incorrect installation procedures or using incompatible versions of libraries and dependencies.

   • Users may inadvertently install versions that do not align with their current setup.

5. Environmental Factors: Issues related to the operating system or hardware configurations, such as insufficient permissions or corrupted installations.

   • Environmental inconsistencies can affect library accessibility and lead to errors.

Troubleshooting Steps, Solutions & Fixes

1. Verify Library Installation:

   • Run the following command to check if libopenblas is installed:

     dpkg -l | grep libopenblas
     

   • If it is missing, install it using:

     sudo apt-get install libopenblas-base
     

2. Check Library Links:

   • Use ldd to verify if libopenblas.so.0 is correctly linked:

     ldd /usr/local/lib/python3.8/dist-packages/torch/_C.cpython-38-aarch64-linux-gnu.so
     

   • Ensure that libopenblas.so.0 appears in the output without any "not found" messages.

3. Inspect Environment Variables:

   • Check if LD_LIBRARY_PATH includes paths where libraries are installed:

     echo $LD_LIBRARY_PATH
     

   • If necessary, add the library path:

     export LD_LIBRARY_PATH=/usr/lib/aarch64-linux-gnu:$LD_LIBRARY_PATH
     

4. Use Docker Container:

   • As a workaround, consider using pre-configured Docker containers that include PyTorch and its dependencies:

     docker run --rm --gpus all nvcr.io/nvidia/l4t-pytorch:<version>
     

   • This can simplify dependency management and ensure compatibility.

5. Reinstall PyTorch:

   • If issues persist, consider reinstalling PyTorch to ensure that all dependencies are correctly set up:

     pip uninstall torch
     pip install torch --extra-index-url https://download.pytorch.org/whl/torch_stable.html
     

6. Check JetPack Version:

   • Confirm you are using a compatible version of JetPack (5.1 or later) by running:

     cat /etc/nv_tegra/release
     

7. Consult Documentation & Community:

   • Refer to Nvidia’s official documentation for any updates regarding library dependencies or installation instructions.
   • Engage with community forums for additional insights or similar experiences from other users.

8. Log Exporting for Further Analysis:

   • If problems persist after following these steps, export logs from your setup for further troubleshooting assistance from Nvidia support or community forums.

By following these troubleshooting steps, users can systematically address the issue surrounding libopenblas.so.0 not being found on their Nvidia Jetson Orin Nano boards, enabling them to utilize PyTorch effectively for their projects.