Broken pipe

[ryan994]

Hello, when I try to run v4.0, I meet a issue, maybe a bug?

🐛 Bug

BrokenPipeError: [Errno 32] Broken pipe

To Reproduce

I did not change anything, the commond I run is :
python train.py --batch 20 --epochs 300 --data ./data/coco128.yaml --weights ./weights/yolov5s.pt --name test123

Output:

Traceback (most recent call last):
File "train.py", line 519, in
train(hyp, opt, device, tb_writer, wandb)
File "train.py", line 202, in train
rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0]
File "D:\yolov5_v4.0\utils\datasets.py", line 83, in create_dataloader
collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
File "D:\yolov5_v4.0\utils\datasets.py", line 96, in init
self.iterator = super().iter()
File "D:\Anaconda3\envs\pytorch17\lib\site-packages\torch\utils\data\dataloader.py", line 352, in iter
return self._get_iterator()
File "D:\Anaconda3\envs\pytorch17\lib\site-packages\torch\utils\data\dataloader.py", line 294, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "D:\Anaconda3\envs\pytorch17\lib\site-packages\torch\utils\data\dataloader.py", line 801, in init
w.start()
File "D:\Anaconda3\envs\pytorch17\lib\multiprocessing\process.py", line 112, in start
self._popen = self._Popen(self)
File "D:\Anaconda3\envs\pytorch17\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "D:\Anaconda3\envs\pytorch17\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "D:\Anaconda3\envs\pytorch17\lib\multiprocessing\popen_spawn_win32.py", line 89, in init
reduction.dump(process_obj, to_child)
File "D:\Anaconda3\envs\pytorch17\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
BrokenPipeError: [Errno 32] Broken pipe

Environment

• OS: win10
• GPU Using torch 1.7.1+cu101 CUDA:0 (GeForce GTX 1070, 8192.0MB)
• Memory: 16

Additional context

I google this issue, it seems about thread, when I change number of workers to 6 or below, it worked successfully. Is my computer issue or a bug? Anyone has idea? Thanks!!!

[github-actions]

👋 Hello @ryan994, 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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[glenn-jocher]

@ryan994 yes this may be associated with multiple dataloader workers. I don't think there's any relation to the recent release. If you can reproduce this in a Colab notebook please advise, otherwise your solution should work locally.

[ryan994]

@glenn-jocher I run v3.0 before, It works prefectly with 8 workers, however, it does not work in v4.0 at same env.

[glenn-jocher]

@ryan994 if you can supply a reproducible example in a common environment (one of the 4 above), we can take a look.

[glenn-jocher]

@ryan994 also just an FYI we are training several models at the moment with v4.0 release with default worker counts with no known issues other than #1852, which is unrelated.

[ryan994]

@glenn-jocher ok, I will try to run it on other pc, and check if my pc's problem.

[glenn-jocher]

@ryan994 ok! docker image is a good solution also for local environment issues:
https://hub.docker.com/r/ultralytics/yolov5
https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/