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Multigpu training becomes slower in Kaggle #10078
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👋 Hello @BraunGe, 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. If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com. RequirementsPython>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
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@BraunGe this is incorrect multi-GPU usage. See Multi-GPU tutorial for correct usage: Tutorials
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Hello,
Recently, Kaggle begun to provide T42 GPU option. However, I found that when I train the YOLOv5s with single P100, it is much faster than T42. The batch size for P100 is 64, for T4*2 is 128 (64 each).
In my mind, if we double the batch size, it could run faster.
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