Running yolo with GPU

[usagi123]

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Question

I am just started learning how to use yolo and just trying out by follow quickstart guide. As I wanted to export model yolov8n.pt to engine by using yolo export model=yolov8n.pt format=engine which required pytorch with cuda as yolo returns

torch.cuda.is_available(): False
torch.cuda.device_count(): 0
os.environ['CUDA_VISIBLE_DEVICES']: None

So I double check that and it returns true

Python 3.11.3 (tags/v3.11.3:f3909b8, Apr  4 2023, 23:49:59) [MSC v.1934 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
True
>>> torch.__version__
'2.3.0+cu121'

So I am confused. How my machine can both have and cannot have gpu. Not sure if this is related but I noticed yolo shown Ultralytics YOLOv8.2.15 🚀 Python-3.10.9 torch-2.1.0 while I am using python 3.11.3 and torch 2.3.0. Maybe that's the reason?

Further searching, I found this issue #4144 which the solution is to use "CUDA-enabled PyTorch build" is what I was using by installing pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 which generated from pytorch getting started locally

Additional

No response

[github-actions]

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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[glenn-jocher]

Hello! It looks like you're encountering an issue where your environment is not consistently recognizing the GPU. This could indeed be related to the different Python and PyTorch versions you're using compared to those reported by YOLOv8.

Here are a couple of steps to help resolve this:

1. Environment Consistency: Ensure that the Python environment from which you're running the YOLO command is the same one where torch.cuda.is_available() returns True. Sometimes, different terminals or IDEs might use different environments.

2. Version Compatibility: YOLOv8 might have specific requirements or better compatibility with certain versions of Python and PyTorch. You might want to try aligning your environment more closely with the versions YOLO reports (Python-3.10.9 and torch-2.1.0). You can create a virtual environment to test this without affecting your current setup:

   python -m venv yolovenv
   yolovenv\Scripts\activate  # Windows
   pip install torch==2.1.0+cu121 torchvision torchaudio

3. Check CUDA Visibility: Before running the YOLO command, ensure that CUDA devices are visible in your session:

   import torch
   print(torch.cuda.is_available())
   print(torch.cuda.device_count())
   print(os.environ['CUDA_VISIBLE_DEVICES'])

If these steps don't resolve the issue, it might be helpful to reinstall the CUDA-enabled PyTorch build ensuring it matches the CUDA version installed on your machine. Sometimes, a fresh installation can clear up any discrepancies.

Hope this helps! Let me know if you have any more questions. 😊

[usagi123]

Thank you so much, I just uninstall both of them and reinstall and everything fixed.

[glenn-jocher]

Great to hear that a reinstall fixed the issue! If you encounter any more questions or need further assistance as you continue exploring YOLOv8, feel free to reach out. Happy detecting! 😊