Can’t install h5py nor libhdf5-dev

Issue Overview

Users are experiencing difficulties installing the h5py library and the libhdf5-dev package while working with the Nvidia Jetson Orin Nano Dev board. The issue arises during the Docker build process, specifically when attempting to install dependencies listed in a requirements.txt file.

Symptoms

• Error messages indicating that the HDF5 library cannot be loaded:
  • "Unable to load dependency HDF5, make sure HDF5 is installed properly."
  • "libhdf5.so: cannot open shared object file: No such file or directory."
• When attempting to install libhdf5-dev and libhdf5-serial-dev, users encounter a 404 error indicating that certain packages cannot be fetched from the repository.

Context

• The problem occurs during the Docker build process using a Dockerfile that specifies a base image for TensorRT.
• The user is trying to install TensorFlow version 2.13.1, which depends on HDF5.
• The issue appears to be related to package availability in the Ubuntu repositories for the architecture being used (arm64).

Hardware and Software Specifications

• Base image used: nvcr.io/nvidia/l4t-tensorrt:r8.4.1.5-devel
• JetPack version: R35 (as identified by users in the forum).
• Attempted installation commands involve standard APT commands for package management.

Frequency and Impact

The issue seems to occur consistently for users trying to set up their development environment with similar configurations. It significantly impacts user experience by preventing the installation of necessary libraries for machine learning applications, thereby hindering development efforts.

Possible Causes

• Hardware Incompatibilities or Defects: While unlikely, issues with hardware could potentially affect library installations.

• Software Bugs or Conflicts: There may be conflicts between different versions of libraries required by TensorFlow and those available in the repositories.

• Configuration Errors: Incorrect configuration in the Dockerfile or missing dependencies could lead to installation failures.

• Driver Issues: Outdated or incompatible drivers for the Jetson platform could affect library functionality.

• Environmental Factors: Network issues or repository changes could lead to 404 errors when fetching packages.

• User Errors or Misconfigurations: Users may not be using compatible versions of software or may have incorrect paths specified in their Dockerfile.

Troubleshooting Steps, Solutions & Fixes

1. Verify JetPack Version:

   • Run the command:

     cat /etc/nv_tegra_release
     

   • Ensure that you are using a compatible JetPack version (e.g., 5.0.2).

2. Update APT Repository:

   • Before installing packages, ensure that your package lists are up-to-date:

     apt-get update
     

3. Check Package Availability:

   • If encountering 404 errors, verify if the required packages are available for your architecture by checking:

     apt-cache search libhdf5
     

4. Modify Dockerfile:

   • Adjust your Dockerfile to include necessary packages:

     RUN apt-get update && \
         apt-get install --no-install-recommends -y libhdf5-dev libhdf5-serial-dev && \
         apt-get clean && rm -rf /var/lib/apt/lists/*
     

5. Rebuild TensorRT Engine:

   • If encountering compatibility issues with TensorRT versions, rebuild your model with the current version of TensorRT specified in your Docker image.

6. Use Alternative Base Image:

   • If problems persist, consider switching to a different base image that is known to work with your setup.

7. Check Network Configuration:

   • Ensure that there are no firewall rules or network issues preventing access to package repositories.

8. Consult Documentation and Community Resources:

   • Refer to Nvidia’s official documentation for Jetson and TensorRT for any specific installation instructions or known issues.
   • Engage with community forums for additional support and shared experiences from other users facing similar issues.

9. Best Practices for Future Installations:

   • Always check compatibility between software versions before installation.
   • Maintain an updated list of dependencies and their corresponding versions in your project documentation.

By following these steps, users should be able to diagnose and resolve issues related to installing h5py and libhdf5-dev on the Nvidia Jetson Orin Nano Dev board effectively.