Calculate GPU requirements at given batch size and image size

[VishalBalaji321]

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Question

Hi, I wanted to train YoloV5 (m6, l6 and x6) on custom dataset and I am often running into memory constraints. For a single GPU RTX Quadro 4000 (8GB), I am able to train yolov5l6 only at batch size 3 and image size 1920, which I understand from the forums to be very suboptimal. Reducing image size is (I think) not a viable option, since I am working with compressed images and small objects.
Is it possible to give an approximation of required GPU memory of for a given model at given batch size and image size?
If it is not possible to generalize, I would like to know specifically for batch size 16, 24 and 32 for l6 and x6 at img size 1920.

Any suggestion is highly appreciated😃

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[github-actions]

👋 Hello @VishalBalaji321, 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>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

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

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[glenn-jocher]

👋 Hello! Thanks for asking about CUDA memory issues. YOLOv5 🚀 can be trained on CPU, single-GPU, or multi-GPU. When training on GPU it is important to keep your batch-size small enough that you do not use all of your GPU memory, otherwise you will see a CUDA Out Of Memory (OOM) Error and your training will crash. You can observe your CUDA memory utilization using either the nvidia-smi command or by viewing your console output:

CUDA Out of Memory Solutions

If you encounter a CUDA OOM error, the steps you can take to reduce your memory usage are:

• Reduce --batch-size
• Reduce --img-size
• Reduce model size, i.e. from YOLOv5x -> YOLOv5l -> YOLOv5m -> YOLOv5s
• Train with multi-GPU at the same --batch-size
• Upgrade your hardware to a larger GPU
• Train on free GPU backends with up to 16GB of CUDA memory:

AutoBatch

You can use YOLOv5 AutoBatch (NEW) to find the best batch size for your training by passing --batch-size -1. AutoBatch will solve for a 90% CUDA memory-utilization batch-size given your training settings. AutoBatch is experimental, and only works for Single-GPU training. It may not work on all systems, and is not recommended for production use.

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

[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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