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Yolov8 error while training on gpu #227
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@MuhammadSibtain5099 please use device=0, like other args, arg=value. More details please read our Docs. :) |
@Laughing-q see the first line of the screenshot. I am already using device=0. Is there any mistake? |
@MuhammadSibtain5099 ohh it looks your cuda device is unavailable, can you check |
@AyushExel we need update the assert msg. |
@Laughing-q No. it is returning False |
@MuhammadSibtain5099 your torch is cpu version and you have to install torch corresponding to your cuda version then you're free to use your GPU for training. |
Try to install |
Looks like its a cuda version mismatch issue? I'll close this but please open if there any other issue |
hi how can I use gpu:1 for training? gpu: 0 is busy. no matter how I set the device, the train is running on gpu:0 leading to memory error , torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB (GPU 0; 10.92 GiB total capacity; 9.81 GiB already allocated; 48.25 MiB free; 9.88 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF |
@creativesh hi, To use a different GPU for training in YOLOv8, you need to specify the GPU device index in the However, if GPU:0 is already busy, changing the device index alone may not solve the memory error issue. The error message indicates that CUDA is running out of memory on GPU:0. You may need to consider reducing the batch size or model size to fit the available memory on GPU:0. Alternatively, you can try optimizing your code or freeing up memory on GPU:0 to make more memory available. Please note that YOLOv8 itself does not have specific functionality for automatically balancing the memory usage across multiple GPUs. It's up to the user to manage the GPU resources and ensure the models and data fit within the available memory. I hope this helps! Let me know if you have any further questions. |
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@ChearLX hello, If your machine correctly identifies the GPU but your code fails to utilize it, there could be multiple potential reasons. Here are a few possibilities:
Please check these potential issue areas and let us know if you're still facing issues. Best, |
@glenn-jocher Hi, |
Looking at your screenshots, I suspect the issue lies with your PyTorch installation. From your last screenshot, it looks like you have PyTorch installed for CPU ( Please uninstall your current version and then reinstall PyTorch using the right CUDA version. Once done, kindly check the output of Let me know if this resolves your issue. If not, please provide the new error messages or issues you're facing. Best, |
@BarsikArsik hello! No worries, we all start somewhere, and it's great you're diving into AI programming. 🌟 From what you've shared, it looks like there might be a mismatch between your CUDA version and the PyTorch version. As of my last check, PyTorch doesn't have a release for CUDA 12.4 yet. The error when setting Could you try installing PyTorch specifically for your CUDA version (if you're using CUDA 12.1 as mentioned)? Here's a generic command, but please adjust for the exact versions: pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 If CUDA 12.4 is a must, you might need to keep an eye on the PyTorch official site or GitHub for updates on support for this version. For running inference with GPU, ensuring your Feel free to reach back if you're still encountering the error. Happy coding! 🚀 |
Hey 😊! Great to hear you managed to install CUDA 12.1. To resolve the GPU transfer issue, ensure PyTorch links to the correct CUDA version. You can verify this in Python: import torch
print(torch.__version__)
print(torch.cuda.is_available()) If pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 Remember to restart your environment after reinstalling. Let's keep things moving swiftly, even on the GPU side of things! 🚀 |
Hey there! It seems like there's an issue, but don't worry, we're here to help! If you're experiencing trouble with GPU utilization, let's ensure PyTorch is correctly recognizing your CUDA setup: Firstly, check if PyTorch can see your GPU: import torch
print(torch.cuda.is_available()) If it returns pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 Change |
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Question
-- device=0 is not working to train on GPU
error: unrecognized arguments: --device 0
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