GPU utilization is low when training on COCO dataset

[ice-tall-cold]

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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]

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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.

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Access additional Ultralytics ⚡ resources:

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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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[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!