GPU utilization is low when training on COCO dataset

[ice-tall-cold]

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Question

Hi,
I'm using 3090ti to train yolov5s. When I train with COCO128, it's pretty fast and GPU utilization is high. But when I train with COCO dataset, it's much slower and GPU utilization is also low. I search on the website and realize it might be that the data read speed is low and GPU is always waiting. Then I use the new SSD and it increases the training speed and GPU utilization a little. But it still takes one and a half hours to train one epoch. Do you know what else I can do to further increase the training speed and GPU utilization? Appreciate it if you can provide any advice.

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[github-actions]

👋 Hello @ice-tall-cold, 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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[ice-tall-cold]

And I already train on cached data and tried increasing the batch size.

[glenn-jocher]

👋 Hello! Thanks for asking about training speed issues. YOLOv5 🚀 can be trained on CPU (slowest), single-GPU, or multi-GPU (fastest). If you would like to increase your training speed some options are:

• Increase --batch-size
• Reduce --img-size
• Reduce model size, i.e. from YOLOv5x -> YOLOv5l -> YOLOv5m -> YOLOv5s
• Train with multi-GPU DDP at larger --batch-size
• Train on cached data: python train.py --cache (RAM caching) or --cache disk (disk caching)
• Train on faster GPUs, i.e.: P100 -> V100 -> A100
• Train on free GPU backends with up to 16GB of CUDA memory:

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

[MartinPedersenpp]

What is your batch size?

[ice-tall-cold]

What is your batch size?

Now I'm using 64. And it gets better than before.

[github-actions]

👋 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.

Access additional YOLOv5 🚀 resources:

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Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
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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!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[glenn-jocher]

@ice-tall-cold glad to hear that increasing the batch size has improved the training speed! 🚀 Larger batch sizes can often help utilize the GPU more efficiently. If you have any more questions or need further assistance, feel free to ask!