-- device command returns OOM, runs fine without it

[GGDRriedel]

Search before asking

• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Training, Evolution

Bug

RuntimeError: CUDA out of memory. Tried to allocate 126.00 MiB (GPU 0; 10.76 GiB total capacity; 9.45 GiB already allocated; 93.69 MiB free; 9.57 GiB reserved in total by PyTorch)

Environment

YOLOv5  v6.0-134-gc45f9f6 torch 1.8.1+cu102 CUDA:0 (GeForce RTX 2080 Ti, 11019MiB)

Minimal Reproducible Example

python train.py --epochs 10 --data gpr_highway.yaml --weights yolov5x6.pt --cache --evolve 10 --device 0

Gives the OOM error

python train.py --epochs 10 --data gpr_highway.yaml --weights yolov5x6.pt --cache --evolve 10

Runs just fine.

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[GGDRriedel]

I've noticed when it runs "just fine" it acutally uses both GPUs.

This should maybe be reflected in the tutorial section in
https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution

The section

# Multi-GPU
for i in 0 1 2 3; do
  nohup python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --evolve --device $i > evolve_gpu_$i.log &
done

seems not up to date anymore

[glenn-jocher]

@GGDRriedel 👋 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!

[GGDRriedel]

Hey Glenn, the issue isn't really the OOM itself, rather it's that the current wiki entry doesn't apply to the current library status anymore as the --evolve parameter seems to use multiple GPUs anyway

[glenn-jocher]

@GGDRriedel device is established here, evolve status is not relevant to its definition.

yolov5/train.py

Line 511 in c1249a4

device = select_device(opt.device, batch_size=opt.batch_size)

We are not able to reproduce any device issues. If you believe you have a reproducible issue in a common environment please raise a bug report with code to reproduce.

We've created a few short guidelines below to help users provide what we need in order to get started investigating a possible problem.

How to create a Minimal, Reproducible Example

When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to reproduce the problem. This is referred to by community members as creating a minimum reproducible example. Your code that reproduces the problem should be:

• ✅ Minimal – Use as little code as possible to produce the problem
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• ✅ Current – Verify that your code is up-to-date with GitHub master, and if necessary git pull or git clone a new copy to ensure your problem has not already been solved in master.
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If you believe your problem meets all the above criteria, please close this issue and raise a new one using the 🐛 Bug Report template with a minimum reproducible example to help us better understand and diagnose your problem.

Thank you! 😃

[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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