Symbol resolution conflicts with Triton Server for Jetpack TensorFlow backend (gRPC, protobuf, absl, etc.)

Issue Overview

Users of the Nvidia Jetson Orin Nano Dev board running Jetson Linux with JetPack installed are experiencing symbol resolution conflicts when attempting to utilize Triton Server within C++ applications via the In-Process API. The core issue arises when the application, which relies on custom-built versions of libraries such as gRPC, Protobuf, and absl, encounters crashes during TensorFlow model loading due to conflicting symbol resolutions.

Symptoms

• Crashes occur during the execution of TRITONSERVER_ServerNew when loading TensorFlow models.
• LD_DEBUG logs indicate that symbols from user-defined libraries are being resolved instead of those baked into Triton Server’s TensorFlow support libraries.

Context

• The problem manifests when using custom-built versions of libraries located in /usr/local, which conflict with the versions used by Triton Server.
• The issue is consistent and reproducible when the application links against the framework that introduces these dependencies.

Specifications

• Jetson Orin Nano Dev Kit
• Jetson Linux with JetPack
• Custom-built libraries: gRPC, Protobuf, absl

Impact

This conflict severely hampers the ability to run TensorFlow models within Triton Server, leading to a blocked development process and potential delays in project timelines.

Possible Causes

• Library Version Mismatch: The versions of gRPC, Protobuf, and absl used in the application do not match those used to build the Triton Server’s TensorFlow backend.

• Symbol Resolution Behavior: The default behavior of dlopen may lead to user-defined symbols taking precedence over those in shared libraries loaded by Triton Server.

• Lack of Build Integration: The absence of headers and build system integration files for the necessary libraries in the JetPack-specific Triton tarball complicates compatibility.

• Environmental Factors: Issues related to library paths or environment variables that may influence how shared libraries are loaded.

Troubleshooting Steps, Solutions & Fixes

1. Verify Library Versions:

   • Check the versions of gRPC, Protobuf, and absl used by Triton Server. This can be done by inspecting the source or documentation available on GitHub.
   • Ensure your application uses matching versions.

2. Use LD_DEBUG for Diagnostics:

   • Run your application with LD_DEBUG=bindings ./your_application to observe how symbols are being resolved.
   • Analyze the output to identify any unexpected bindings.

3. Build Framework Against Triton Versions:

   • If feasible, modify your framework to use the same versions of gRPC and other dependencies as those used in Triton Server.
   • This may require building from source if precompiled binaries are not available.

4. Consider Using dlmopen:

   • Experiment with using dlmopen instead of dlopen for loading libtritonserver.so with LM_ID_NEWLM. This can help isolate symbols within separate link maps.
   • Example command:

     void* handle = dlmopen(LM_ID_NEWLM, "libtritonserver.so", RTLD_NOW);
     

5. File an Issue on GitHub:

   • If you suspect a bug or oversight in how Triton Server handles symbol resolution, consider filing an issue on GitHub for further assistance from the development team.

6. Explore Docker Images:

   • Investigate whether JetPack-compatible Docker images for Triton Server provide a more complete set of dependencies that could resolve compatibility issues.
   • Check for available headers and build system integration files.

7. Build from Source:

   • As a last resort, consider building Triton Server from source along with its TensorFlow backend against your custom library versions. This approach may be complex but could provide a tailored solution.

8. Documentation and Community Support:

   • Consult Nvidia’s official documentation for any updates regarding library compatibility.
   • Engage with community forums or support channels for shared experiences and solutions from other users facing similar issues.

By following these steps, users should be able to diagnose and potentially resolve the symbol resolution conflicts encountered while using Triton Server on the Nvidia Jetson Orin Nano Dev board.