H5py Module Error on Nvidia Jetson Orin Nano with JetPack 6

Issue Overview

Users of the Nvidia Jetson Orin Nano development board running JetPack 6 are experiencing problems with the h5py module after installing TensorFlow 2.15.1. The specific issues reported include:

• Errors when loading pre-trained models
• Inability to view the h5py version
• The import h5py operation succeeds, but version display fails

These issues are impacting the ability to work with HDF5 files and pre-trained models, which are crucial for many machine learning and data processing tasks on the Jetson platform.

Possible Causes

1. Incompatible h5py version: The installed h5py version may not be compatible with the TensorFlow 2.15.1 or the JetPack 6 environment.

2. Missing dependencies: Required libraries or dependencies for h5py might be missing or incompatible with the current system configuration.

3. JetPack 6 compatibility issues: As JetPack 6 is a relatively new release, there might be compatibility issues with certain Python packages or libraries.

4. Python environment conflicts: Conflicts between different Python environments or package versions could lead to import and version display problems.

5. Incomplete or corrupted installation: The h5py module might have been installed incorrectly or incompletely.

Troubleshooting Steps, Solutions & Fixes

1. Verify h5py installation:

   • Open a terminal and run:

     pip list | grep h5py
     

   • If h5py is not listed, install it using:

     pip install h5py
     

2. Check h5py version compatibility:

   • Ensure the installed h5py version is compatible with TensorFlow 2.15.1. You may need to install a specific version:

     pip install h5py==3.1.0
     

3. Reinstall h5py:

   • Uninstall and reinstall h5py:

     pip uninstall h5py
     pip install h5py
     

4. Verify HDF5 library installation:

   • Check if the HDF5 library is installed:

     sudo apt-get install libhdf5-dev
     

5. Update system packages:

   • Update and upgrade system packages:

     sudo apt-get update
     sudo apt-get upgrade
     

6. Check Python environment:

   • Ensure you’re using the correct Python environment:

     which python
     python --version
     

7. Compile h5py from source:
   If pre-built packages are causing issues, try compiling h5py from source:

   pip uninstall h5py
   pip install Cython
   pip install --no-binary=h5py h5py
   

8. Verify TensorFlow installation:

   • Ensure TensorFlow is correctly installed:

     python -c "import tensorflow as tf; print(tf.__version__)"
     

9. Check system logs:

   • Examine system logs for any relevant error messages:

     dmesg | tail
     journalctl -xe
     

10. Provide detailed error logs:

    • When seeking further assistance, copy and paste the complete error logs, including any tracebacks or system information, to help diagnose the issue more accurately.

11. Consider JetPack version:

    • If the issue persists, consider reverting to a previous, stable JetPack version or waiting for updates that address compatibility issues with JetPack 6.

12. Monitor official forums and documentation:

    • Keep an eye on NVIDIA’s official forums and documentation for any known issues or updates regarding h5py compatibility with JetPack 6 and TensorFlow 2.15.1.

If these steps do not resolve the issue, it’s recommended to open a new support ticket with detailed information about your system configuration, exact error messages, and steps to reproduce the problem.