NCCL Support for PyTorch on Jetson Orin Nano

Issue Overview

Users of the Jetson Orin Nano are experiencing difficulties with NCCL (NVIDIA Collective Communications Library) support for PyTorch. Specifically, when testing distributed capabilities, torch.distributed.is_available() returns True, but torch.distributed.is_nccl_available() returns False. This issue occurs after installing PyTorch v1.11.0 on the Jetson Orin Nano.

Attempts to build PyTorch from source using the command python setup.py bdist_wheel have resulted in system hangs and compilation termination errors during the rebuild process. This problem impacts the ability to use distributed computing features in PyTorch on the Jetson Orin Nano, potentially limiting the device’s capabilities for certain machine learning applications.

Possible Causes

1. Hardware architecture limitations: The Jetson Orin Nano is a single-GPU architecture, which may not be compatible with NCCL, as NCCL is designed for multi-GPU systems.

2. Software incompatibility: The installed version of PyTorch (v1.11.0) may not be optimized for the Jetson Orin Nano’s specific hardware configuration.

3. Insufficient system resources: The system hangs during the compilation process could be due to limited memory or swap space on the Jetson Orin Nano.

4. Missing dependencies: The build process may fail due to missing or incompatible dependencies required for NCCL support.

5. Incorrect build configuration: The default build settings may not be appropriate for enabling NCCL support on the Jetson Orin Nano.

Troubleshooting Steps, Solutions & Fixes

1. Verify hardware compatibility:

   • Confirm that your Jetson Orin Nano is a single-GPU architecture device.
   • Understand that NCCL is not supported on single-GPU architectures like the Jetson Orin Nano.

2. Use alternative distributed computing options:

   • Instead of NCCL, consider using MPI (Message Passing Interface) for distributed computing on the Jetson Orin Nano.

3. Recompile PyTorch with distributed support:

   • Enable distributed support by setting the USE_DISTRIBUTED flag during compilation.
   • Use the following command to recompile PyTorch:

     USE_DISTRIBUTED=1 python setup.py bdist_wheel
     

4. Increase swap space to prevent system hangs:

   • If the compilation process hangs, try increasing the swap space on your Jetson Orin Nano.
   • To add 4GB of swap space, use the following commands:

     sudo fallocate -l 4G /swapfile
     sudo chmod 600 /swapfile
     sudo mkswap /swapfile
     sudo swapon /swapfile
     

   • To make the swap space permanent, add this line to /etc/fstab:

     /swapfile none swap sw 0 0
     

5. Use pre-built PyTorch wheels:

   • Instead of building from source, consider using pre-built PyTorch wheels optimized for Jetson devices.
   • Check the NVIDIA Developer website for compatible PyTorch versions for your Jetson Orin Nano.

6. Update JetPack SDK:

   • Ensure you have the latest JetPack SDK installed on your Jetson Orin Nano.
   • Updated SDKs may include optimizations and fixes for PyTorch compatibility.

7. Use alternative distributed computing frameworks:

   • Consider using other distributed computing frameworks that are compatible with single-GPU architectures, such as Horovod or Ray.

8. Monitor system resources:

   • Use tools like htop or nvidia-smi to monitor system resources during the compilation process.
   • This can help identify if resource constraints are causing the build failures.

9. Check for error logs:

   • Examine build logs and error messages for specific issues during the compilation process.
   • Look for missing dependencies or incompatible library versions that may be causing failures.

10. Consult NVIDIA Developer Forums:

    • For ongoing issues or Jetson-specific queries, consider posting on the NVIDIA Developer Forums for expert assistance.

Remember that while NCCL is not supported on the Jetson Orin Nano due to its single-GPU architecture, you can still utilize distributed computing capabilities through alternative methods like MPI. Always ensure you’re using the most up-to-date and compatible software versions for your specific Jetson device.