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Multigpu training becomes slower in Kaggle #10078

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BraunGe opened this issue Nov 8, 2022 · 3 comments
Closed
1 task done

Multigpu training becomes slower in Kaggle #10078

BraunGe opened this issue Nov 8, 2022 · 3 comments
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question Further information is requested Stale Stale and schedule for closing soon

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@BraunGe
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BraunGe commented Nov 8, 2022

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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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@BraunGe BraunGe added the question Further information is requested label Nov 8, 2022
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github-actions bot commented Nov 8, 2022

👋 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.

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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.

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@glenn-jocher
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glenn-jocher commented Nov 8, 2022

@BraunGe this is incorrect multi-GPU usage. See Multi-GPU tutorial for correct usage:

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Good luck 🍀 and let us know if you have any other questions!

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github-actions bot commented Dec 9, 2022

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

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@github-actions github-actions bot added the Stale Stale and schedule for closing soon label Dec 9, 2022
@github-actions github-actions bot closed this as not planned Won't fix, can't repro, duplicate, stale Dec 20, 2022
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