Out of Memory Prompt When Detecting in Real Time

Issue Overview

Users of the Nvidia Jetson Orin Nano Dev board have reported experiencing a persistent "Low memory" warning after approximately 20 minutes of real-time detection. The issue arises while running a detection application utilizing the GPU, specifically with the Ultralytics YOLO model.

Symptoms and Context

• Symptoms: Users receive a "Low memory" warning, indicating insufficient memory for ongoing operations.

• Context: The problem occurs during real-time object detection, which is intended to run for extended periods (hours).

• Hardware/Software Specifications:

  • Device: Nvidia Jetson Orin Nano
  • JetPack Version: 5.1.3
  • Dependencies:
    • torch==2.1.0a0+41361538.nv23.6
    • ultralytics==8.2.38

• Frequency: The issue consistently manifests after about 20 minutes of operation.

• Impact: This memory limitation disrupts the intended functionality of long-duration real-time detection, potentially affecting user projects that rely on continuous operation.

Possible Causes

• Memory Leakage: Continuous increase in memory usage during detection may indicate a memory leak in the application.

• Insufficient Memory Allocation: The available GPU memory (3.3 GB) may not be sufficient for the demands of the detection task, particularly as it runs longer.

• Configuration Errors: Improper setup or configuration of the YOLO model or its dependencies could lead to inefficient memory usage.

• Driver Issues: Outdated or incompatible drivers may contribute to suboptimal performance and memory management.

• Environmental Factors: High temperatures or inadequate power supply could exacerbate memory issues.

Troubleshooting Steps, Solutions & Fixes

Step-by-Step Instructions

1. Monitor Memory Usage:

   • Use tegrastats to monitor GPU memory usage during detection.
   • Command:

     sudo tegrastats
     

2. Check for Memory Leaks:

   • If memory usage keeps increasing, consider using valgrind to identify potential leaks.
   • Command:

     valgrind --leak-check=full python your_detection_script.py
     

3. Export Model to .engine Format:

   • If prompted by Ultralytics support, export your model using the following command:

     model.export(format="engine", workspace=2, int8=True)
     

   • Be aware that this may result in insufficient memory warnings if the device cannot accommodate the tactics required.

4. Optimize Model Configuration:

   • Review and adjust model parameters to optimize for lower memory usage.
   • Consider using a smaller model variant if available.

5. Update Software and Drivers:

   • Ensure that all software dependencies are up to date, including JetPack and CUDA drivers.
   • Check for any available firmware updates for the Jetson Orin Nano.

6. Test with Different Configurations:

   • Experiment with different hardware configurations (e.g., reducing other processes running on the device) to isolate the issue.

Recommended Solutions

• If multiple users report success with monitoring tools like tegrastats and valgrind, these are recommended as effective diagnostic steps.

Best Practices

• Regularly monitor system resources during long-running tasks to catch issues early.

• Keep software and dependencies updated to leverage improvements in performance and bug fixes.

Unresolved Aspects

• Further investigation may be needed into specific configurations or code implementations that could lead to memory leaks.

• Users should consider sharing their findings on forums or with Ultralytics support for community-driven solutions.