in _ddp_init_helper expect_sparse_gradient) RuntimeError: Model replicas must have an equal number of parameters.

[AIYoungcino]

在hpc上训练YOLOv5，单节点单卡可以运行，但单节点多卡，多节点多卡都会报错。

[github-actions]

👋 Hello @xioyung, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[AIYoungcino]

/public/software/apps/DeepLearning/PyTorch/rocm3.3_torch1.5/lib/python3.6/site-packages/torch/nn/parallel/distributed.py:303: UserWarning: Single-Process Multi-GPU is not the recommended mode for DDP. In this mode, each DDP instance operates on multiple devices and creates multiple module replicas within one process. The overhead of scatter/gather and GIL contention in every forward pass can slow down training. Please consider using one DDP instance per device or per module replica by explicitly setting device_ids or CUDA_VISIBLE_DEVICES. NB: There is a known issue in nn.parallel.replicate that prevents a single DDP instance to operate on multiple model replicas.
"Single-Process Multi-GPU is not the recommended mode for "
Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5x.yaml', data='./data/mydata.yaml', device='1', epochs=300, img_size=[640], multi_scale=False, name='', noautoanchor=False, nosave=False, notest=False, rect=False, single_cls=False, weights='weights/last.pt')
Using CUDA device0 _CudaDeviceProperties(name='Device 66a1', total_memory=16368MB)
device1 _CudaDeviceProperties(name='Device 66a1', total_memory=16368MB)
device2 _CudaDeviceProperties(name='Device 66a1', total_memory=16368MB)
device3 _CudaDeviceProperties(name='Device 66a1', total_memory=16368MB)

[NanoCode012]

@xioyung, pull latest code base and check Multi-GPU Tutorial https://docs.ultralytics.com/yolov5

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.