Tensorflow v2.16.1 GPU Version Local Build on Jetson Orin Nano Failed

Issue Overview

Users are experiencing build failures when attempting to compile TensorFlow v2.16.1 for GPU on the Nvidia Jetson Orin Nano development board. The specific error message indicates issues related to CUDA compatibility and unsupported GPU architecture:

• Error Message:
  • clang: warning: CUDA version is newer than the latest supported version 11.5
  • clang: error: unsupported CUDA gpu architecture: sm_90

The problem occurs during the local build process, specifically when compiling the file cuda_conditional_kernels.cu.cc. This issue is reported consistently by multiple users, indicating that it is not an isolated incident.

Context

• Hardware: Nvidia Jetson Orin Nano
• Software: TensorFlow v2.16.1, Nvidia Jetpack version 6.0-b52
• Frequency: Multiple users have reported similar issues, suggesting a common problem across setups.
• Impact: Users are unable to utilize TensorFlow for GPU tasks, significantly hindering their development efforts.

Possible Causes

• Hardware Incompatibilities: The Jetson Orin Nano may not fully support the CUDA architecture specified (sm_90) in TensorFlow v2.16.1.

• Software Bugs or Conflicts: There could be unresolved bugs in TensorFlow v2.16.1 or conflicts with the installed CUDA version.

• Configuration Errors: Incorrect build configurations or environment settings might lead to compilation failures.

• Driver Issues: The installed drivers may not be compatible with the CUDA version required by TensorFlow v2.16.1.

• User Errors or Misconfigurations: Users may not be following the correct build procedures as outlined in documentation.

Troubleshooting Steps, Solutions & Fixes

1. Verify Compatibility:

   • Ensure that the installed CUDA version is compatible with TensorFlow v2.16.1.
   • Check the official TensorFlow documentation for supported CUDA versions.

2. Use Prebuilt Packages:

   • If building from source is not essential, consider using prebuilt packages available from Nvidia’s repository:

     sudo pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v60dp tensorflow==2.16.1+nv...
     

3. Adjust Build Configuration:

   • Modify the build configuration to target a supported architecture if using custom builds:
     • Edit the BUILD files to specify a compatible architecture (e.g., sm_80).

4. Gather System Information:

   • Use the following command to check current CUDA and driver versions:

     nvcc --version
     nvidia-smi
     

5. Run Build with Verbose Failures:

   • To gain more insights into the build failure, use:

     bazel build --verbose_failures //tensorflow/tools/pip_package:build_pip_package
     

6. Consult Documentation and Community Resources:

   • Refer to the official TensorFlow building guide for Jetson devices.
   • Engage with community forums for shared experiences and solutions.

7. Check for Updates:

   • Ensure that all packages, including Jetpack and TensorFlow, are up-to-date.

8. Consider Alternative Versions:

   • If issues persist, consider using an earlier version of TensorFlow (e.g., 2.15) that may have better compatibility with existing setups.

9. Report Issues to TensorFlow Team:

   • If none of the above solutions work, report detailed findings to the TensorFlow team for further investigation.

Unresolved Aspects

• There is no public document available detailing how Nvidia builds their TensorFlow packages specifically for Jetson devices, which could help users replicate successful builds.

• Users have noted inconsistencies between performance results on different platforms (e.g., Colab vs local builds), which warrants further investigation into potential discrepancies in library versions or configurations used by Nvidia in their prebuilt binaries versus community builds.

By following these steps and recommendations, users should be able to troubleshoot and potentially resolve issues related to building TensorFlow v2.16.1 on the Nvidia Jetson Orin Nano development board effectively.