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Calculate GPU requirements at given batch size and image size #5528

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VishalBalaji321 opened this issue Nov 6, 2021 · 3 comments
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@VishalBalaji321
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VishalBalaji321 commented Nov 6, 2021

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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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@VishalBalaji321 VishalBalaji321 added the question Further information is requested label Nov 6, 2021
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github-actions bot commented Nov 6, 2021

👋 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.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

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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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YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

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If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

@glenn-jocher
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glenn-jocher commented Nov 6, 2021

👋 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:

Screenshot 2021-05-28 at 12 19 51

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: Open In Colab Open In Kaggle

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.

Screenshot 2021-11-06 at 12 31 10

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

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github-actions bot commented Dec 7, 2021

👋 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:

Access additional Ultralytics ⚡ resources:

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 ⭐!

@github-actions github-actions bot added the Stale Stale and schedule for closing soon label Dec 7, 2021
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