Running yolo with GPU about ultralytics HOT 4 CLOSED

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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

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

from ultralytics.

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. 😊

from ultralytics.

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

from ultralytics.

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! 😊

from ultralytics.