Скачать или смотреть Understanding the RuntimeError: No CUDA GPUs are available in PyTorch on Windows

Learn why PyTorch may not recognize your GPU on Windows and how to resolve the `RuntimeError: No CUDA GPUs are available` issue, even when your system has a compatible GPU.
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Understanding the RuntimeError: No CUDA GPUs are available in PyTorch on Windows

When working with PyTorch on a Windows machine, you might encounter an error stating, "No CUDA GPUs are available," even if you have a GPU. This issue can be frustrating, especially when you're certain your hardware supports CUDA operations. Let’s dive into the potential reasons behind this error and how you can resolve it.

Potential Causes for the Error

Several factors could lead to PyTorch not recognizing the GPU, causing the RuntimeError: No CUDA GPUs are available:

CUDA Toolkit and Drivers:

Ensure that you have installed the correct version of the CUDA toolkit compatible with your PyTorch version.

Verify that the NVIDIA drivers installed on your system are up-to-date and match the CUDA version. This can be checked via the NVIDIA Control Panel or the nvidia-smi command in the terminal.

PyTorch Installation:

Make sure PyTorch is installed with CUDA support. You can do this by using a package manager like conda or pip with a specific command that includes CUDA.

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or

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Environment Configuration:

Check that your Python environment is correctly set up, especially when using Anaconda. Ensure you’re operating within the environment where PyTorch is installed.

Hardware and BIOS Settings:

On some occasions, hardware configurations or BIOS settings may disable the GPU. Ensure the GPU is enabled and properly recognized by the operating system.

Multiple GPUs:

If you have multiple GPUs, verify that CUDA-aware PyTorch is aiming to use the right GPU. You can explicitly select the device using:

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Steps to Diagnose and Resolve

Here are steps you can take to resolve the issue:

Verify System Recognition:

Use nvidia-smi to check if the system detects the GPU. If it does not appear here, reinstall your NVIDIA drivers.

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Verify PyTorch CUDA Availability:

Ensure PyTorch can see the CUDA installation by running a small check in your Python script:

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Check Package Compatibility:

Make sure the PyTorch, CUDA toolkit, and NVIDIA drivers' versions are compatible. Refer to the PyTorch documentation for the required versions.

Environment Setup:

If you're using Anaconda, ensure you are working within the same virtual environment where you installed PyTorch with CUDA support. Activate your environment with:

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Reinstallation:

If all else fails, reinstall your PyTorch environment with the compatible CUDA version to ensure all dependencies are correctly set up:

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Conclusion

Working with GPUs can significantly accelerate deep learning tasks, making it crucial to resolve issues like PyTorch not recognizing available CUDA GPUs. By ensuring compatibility across the CUDA toolkit, PyTorch installation, and NVIDIA drivers, and meticulously setting up your virtual environment, you can mitigate these issues. Addressing these potential causes will help you get PyTorch up and running with GPU support on your Windows machine, enabling you to harness the power of accelerated computing.