Import failing when torch.distributed is not available

[Mattzi]

💡 Your Question

``Hi,

I am trying to use yolo-nas on a jetson xavier and need super-gradients to do this. Unfortunately the Jetson Torch Wheels (that are needed for cuda support) are built without USE_DISTRIBUTED so torch.distributed.is_available() returns false.

When running

import super_gradients

I get the Error: ImportError: cannot import name 'get_rank' from 'torch.distributed'

Just to double check, to use super_gradients AND have Cuda support available I need to build pytorch from source as torch.distributed is a mandatory requirement for super-gradients?

Versions

PyTorch version: 1.13.0a0+936e9305.nv22.11 Is debug build: False CUDA used to build PyTorch: 11.4 ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (aarch64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: version 3.28.3 Libc version: glibc-2.31

Python version: 3.8.10 (default, Nov 22 2023, 10:22:35) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-5.10.104-tegra-aarch64-with-glibc2.29 Is CUDA available: True CUDA runtime version: 11.4.315 CUDA_MODULE_LOADING set to: GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Probably one of the following: /usr/lib/aarch64-linux-gnu/libcudnn.so.8.6.0 /usr/lib/aarch64-linux-gnu/libcudnn_adv_infer.so.8.6.0 /usr/lib/aarch64-linux-gnu/libcudnn_adv_train.so.8.6.0 /usr/lib/aarch64-linux-gnu/libcudnn_cnn_infer.so.8.6.0 /usr/lib/aarch64-linux-gnu/libcudnn_cnn_train.so.8.6.0 /usr/lib/aarch64-linux-gnu/libcudnn_ops_infer.so.8.6.0 /usr/lib/aarch64-linux-gnu/libcudnn_ops_train.so.8.6.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: False

CPU: Architecture: aarch64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 6 On-line CPU(s) list: 0-5 Thread(s) per core: 1 Core(s) per socket: 2 Socket(s): 3 Vendor ID: Nvidia Model: 0 Model name: ARMv8 Processor rev 0 (v8l) Stepping: 0x0 CPU max MHz: 1907,2000 CPU min MHz: 115,2000 BogoMIPS: 62.50 L1d cache: 384 KiB L1i cache: 768 KiB L2 cache: 6 MiB L3 cache: 4 MiB Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Spec store bypass: Not affected Vulnerability Spectre v1: Mitigation; __user pointer sanitization Vulnerability Spectre v2: Mitigation; Branch predictor hardening Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm dcpop

Versions of relevant libraries: [pip3] numpy==1.23.0 [pip3] onnx==1.15.0 [pip3] onnx-graphsurgeon==0.3.12 [pip3] onnx-simplifier==0.3.5 [pip3] onnxoptimizer==0.3.13 [pip3] onnxruntime==1.15.0 [pip3] onnxsim==0.4.36 [pip3] torch==1.13.0a0+936e9305.nv22.11 [pip3] torch2trt==0.4.0 [pip3] torchmetrics==0.8.0 [pip3] torchvision==0.13.0 [conda] Could not collect