gpu memory usage is low but out of memory

[leooobreak]

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Question

When training yolov5, I found that the GPU memory was not used, GPU memory usage is versy low, and the memory increased very quickly, resulting in out of memory. I would like to know how to use the GPU for training?

command is python .\train.py --device 0 --epochs 1 --batch-size 16

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[github-actions[bot]]

👋 Hello @leooobreak, 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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[glenn-jocher]

@leooobreak hi there!

Thank you for reaching out and providing detailed information about your issue. It sounds like you're encountering an unusual problem where the GPU memory usage is low, but you're still running out of memory. Here are a few steps you can take to troubleshoot and potentially resolve this issue:

1. Verify Environment and Dependencies: Ensure that you are using the latest versions of YOLOv5 and its dependencies. You can update your repository and install the latest requirements with the following commands:

   git pull
   pip install -r requirements.txt

2. Check GPU Utilization: Sometimes, the GPU might not be fully utilized due to various reasons. You can monitor GPU usage using nvidia-smi to see if the GPU is being properly utilized during training.

3. Adjust Batch Size: The batch size you are using might be too large for your GPU memory. Try reducing the batch size to see if it helps:

   python train.py --device 0 --epochs 1 --batch-size 8

4. Use Mixed Precision Training: Mixed precision training can help reduce memory usage and speed up training. You can enable it by adding the --half flag:

   python train.py --device 0 --epochs 1 --batch-size 16 --half

5. Distributed Data Parallel (DDP) Training: If you have multiple GPUs, consider using DistributedDataParallel (DDP) mode for better memory management and performance:

   python -m torch.distributed.run --nproc_per_node 2 train.py --batch 32 --device 0,1

6. Check for Memory Leaks: Ensure that there are no memory leaks in your code or data pipeline. Sometimes, custom data loaders or transformations can cause memory issues.

If the issue persists, please provide more details about your setup, such as the GPU model, CUDA version, and PyTorch version. This information can help in diagnosing the problem more accurately.

Feel free to reach out if you have any further questions or need additional assistance. The YOLO community and the Ultralytics team are here to help! 🚀