**Gstreamer Pipeline issue: PosixMemMap:84 mmap failed**

Issue Overview

Users are experiencing a recurring issue with the Nvidia Jetson Orin Nano Dev board, specifically related to Gstreamer pipelines. The primary symptom is a crash occurring after approximately 1 hour and 30 minutes when running a Python pipeline that utilizes Gstreamer. In contrast, similar pipelines executed via the command line using gst-launch appear to function without limitations. The problem seems to stem from the interaction between the DeepStream Python library and the Gstreamer pipeline setup, particularly when using camera sources. Users have reported that replacing v4l2src with videotestsrc resolves the issue, indicating potential instability with camera sources. The issue has been noted inconsistently across different setups, but it significantly impacts user experience by limiting the functionality of applications relying on long-running video streams.

Possible Causes

1. Hardware Incompatibilities or Defects: The issue may arise from incompatibilities between specific camera hardware and the Jetson Orin Nano, particularly with certain models or configurations.

2. Software Bugs or Conflicts: There may be bugs within the DeepStream Python library or conflicts between different library versions (e.g., JetPack versions) that lead to instability during prolonged operations.

3. Configuration Errors: Incorrect configuration of Gstreamer pipelines or DeepStream settings could cause memory management issues, leading to crashes.

4. Driver Issues: Outdated or incompatible drivers for the camera hardware may contribute to the observed failures in video processing.

5. Environmental Factors: External factors such as power supply fluctuations or overheating could affect the stability of operations over extended periods.

6. User Errors or Misconfigurations: Improper setup of the Gstreamer pipeline in Python scripts could lead to memory leaks or crashes.

Troubleshooting Steps, Solutions & Fixes

1. Diagnosing the Problem:

   • Run the command gst-launch-1.0 v4l2src device=/dev/videoX ! ... directly in the terminal to confirm if it works without crashing.
   • If successful, compare it against your Python implementation to identify discrepancies.

2. Testing Different Sources:

   • Replace v4l2src with videotestsrc in your pipeline code to see if the issue persists. This has been shown to work for extended periods without crashing.
   • Example command:

     gst-launch-1.0 videotestsrc is-live=1 ! nvvidconv ! nvvideoconvert ! nvdsosd ! nv3dsink sync=false
     

3. Isolating the Issue:

   • Test with different camera sources (e.g., RTSP streams) to determine if specific camera hardware is causing instability.
   • Ensure all camera connections are secure and compatible with the Jetson Orin Nano.

4. Updating Software and Drivers:

   • Ensure you are using the latest version of JetPack and DeepStream libraries (consider upgrading from version 5.1.2 to 6.0GA).
   • Follow instructions for building DeepStream Python bindings from source with debug prints enabled to identify issues.

5. Memory Management Checks:

   • Investigate potential memory leaks by reviewing your Python code for dynamic linking/unlinking of Gstreamer elements during runtime.
   • Avoid dynamically linking/unlinking elements; instead, link them once during initialization.

6. Removing Unnecessary Libraries:

   • If you are using PyQt5 in your application, remove it and test again as it may introduce additional memory management issues.

7. Best Practices for Future Prevention:

   • Regularly update all software components involved in your project.
   • Monitor system resources (CPU, memory usage) during operation to identify potential leaks or bottlenecks.

8. Documentation and Resources:

   • Refer to official Nvidia documentation for Gstreamer and DeepStream for detailed configuration options and examples.
   • Check Github repositories related to DeepStream Python applications for community-contributed fixes and enhancements.

9. Unresolved Aspects:

   • Further investigation may be needed regarding specific camera compatibility and how different models interact with the Jetson Orin Nano’s architecture.
   • Users should continue sharing their experiences on forums for collaborative troubleshooting and solutions.

By following these steps, users can systematically address the Gstreamer pipeline issues they are encountering on the Nvidia Jetson Orin Nano Dev board while optimizing their setup for better stability and performance during long-running applications.