Issue for Linux 36.2 docker/run.sh

Issue Overview

Users are experiencing issues when attempting to run the docker/run.sh script on the Nvidia Jetson Orin Nano Dev board with the Linux 36.2 version. The primary symptoms include an error message indicating an "unknown or invalid runtime name: nvidia," which occurs during the execution of the Docker command.

The problem arises in the context of setting up a development environment for Nvidia’s jetson-inference library. Users have confirmed that their system is running L4T BSP Version R36.2.0, as indicated by commands executed in the terminal. The issue seems to be consistent, with multiple users reporting similar errors when trying to run Docker containers that rely on specific runtime configurations.

The impact of this problem significantly hinders user experience, particularly for developers looking to leverage containerized applications for AI and machine learning tasks on the Jetson platform.

Possible Causes

• Hardware Incompatibilities: If the hardware does not support certain Docker features or configurations, it could lead to runtime errors.

• Software Bugs or Conflicts: There may be bugs in the Docker version being used or conflicts with other installed software that affect container execution.

• Configuration Errors: Incorrect settings in Docker or missing configuration files could result in the inability to recognize the Nvidia runtime.

• Driver Issues: Outdated or improperly installed Nvidia drivers may cause Docker to fail in recognizing the necessary runtime.

• Environmental Factors: Insufficient system resources (e.g., memory, CPU) or incorrect permissions could prevent Docker from executing properly.

• User Errors or Misconfigurations: Users may have misconfigured their Docker setup, leading to errors when attempting to run specific commands.

Troubleshooting Steps, Solutions & Fixes

1. Verify Driver Installation:

   • Ensure that the Nvidia drivers are correctly installed and compatible with L4T R36.2.0.
   • Run the following command to check driver status:

     nvidia-smi
     

2. Check Docker Installation:

   • Confirm that Docker is installed and running correctly:

     docker --version
     

3. Inspect Docker Configuration:

   • Verify that the Nvidia runtime is correctly configured in Docker’s settings.
   • Check /etc/docker/daemon.json for the following configuration:

     {
       "runtimes": {
         "nvidia": {
           "path": "nvidia-container-runtime",
           "runtimeArgs": []
         }
       }
     }
     

   • Restart Docker after making changes:

     sudo systemctl restart docker
     

4. Run Diagnostic Commands:

   • Execute diagnostic commands to gather information about the current environment:

     source docker/containers/scripts/l4t_version.sh
     cat /etc/nv_tegra_release
     

5. Test with Different Container Tags:

   • Since there appears to be no CONTAINER_TAG for Linux 36.2, try using a compatible tag such as dustynv/jetson-inference:r35.4.1 if applicable.

6. Update Software Packages:

   • Ensure all software packages are up-to-date, including Docker and any dependencies:

     sudo apt-get update && sudo apt-get upgrade
     

7. Install Missing Plugins:

   • If further issues arise, such as needing gst-v4l2, install it using:

     sudo apt-get install gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-base
     

8. Check System Resources:

   • Ensure sufficient system resources are available (CPU, RAM) and that no other processes are consuming excessive resources.

9. Review Permissions:

   • Check that your user has adequate permissions to run Docker commands without issues.
   • Add your user to the Docker group if necessary:

     sudo usermod -aG docker $USER
     

10. Consult Documentation and Community Forums:

    • Review Nvidia’s official documentation for any updates related to L4T R36 and Docker configurations.
    • Engage with community forums for additional insights or similar experiences from other users.

If multiple users reported success with a particular solution, such as updating the Docker configuration or changing container tags, it is recommended as a first step in troubleshooting this issue.

Unresolved aspects of this issue may include specific compatibility concerns between L4T R36 and existing software environments, warranting further investigation into future updates or patches from Nvidia.