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[WesternTrail]

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There will be errors when the previous version runs. The individual reason is that torch version 1.12.0. I noticed that the torch version is required in the requirement for this update != 1.12.0 to avoid errors during program operation. Actually, I'm running train Py file, the program will automatically check my torch Version (requirements: torch! =1.12.0, >=1.7.0 not found and is required by yolov5, attempting auto update...) And update the torch version. But this update directly installed my torch into the torch1.11.0-CPU version, resulting in a slow training speed. I think you should optimize the code itself rather than limit the torch version, because it will cause a lot of trouble

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[github-actions]

👋 Hello @WesternTrail, 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>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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[zhiqwang]

In favor of not ruling out PyTorch 1.12.0, seems that PyTorch 1.12.0 only have problem for cuda10.2 and some other scenarios, but there are also a lot of people don't use cuda10.2 or don't meet this scenarios today.

• YOLOv5 issues with torch==1.12 on Multi-GPU systems #8395
• Exclude torch==1.12.0, torchvision==0.13.0 (Fix #8395) #8497

[glenn-jocher]

@WesternTrail the torch 1.12 incompatibilities are mainly observed in DDP trainings, but we have introduced this change in requirements.txt and are waiting for 1.12.1 to come out which should hopefully resolve these issues.

If you are not using DDP or cuda 10.2 you should be fine to simply comment out this line in requirements.txt

[glenn-jocher]

@zhiqwang @WesternTrail alternatively we could also introduce an assert torch!=1.12.0 in DDP training with a message to the user to explain the problem.

[zhiqwang]

alternatively we could also introduce an assert torch!=1.12.0 in DDP training with a message to the user to explain the problem.

@glenn-jocher Agreed on this option!

[glenn-jocher]

@zhiqwang good news 😃! Your original issue may now be fixed ✅ in PR #8621. torch==1.12.0 installs are now allowed, and an assert torch!=1.12.0 has been inserted into a new smart_DDP() function. The Docker image is using torch nightly and trains DDP correctly.

To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!