RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable

这个错误通常是由于显卡被其他程序占用或者显卡驱动出现问题导致的。你可以尝试以下几个解决方法：

1. 关闭其他占用显卡的程序，例如游戏或者图形渲染软件等。

2. 检查显卡驱动是否正常，可以尝试重新安装显卡驱动。

3. 如果你正在使用多个GPU进行计算，可以尝试减少使用的GPU数量。

4. 如果以上方法都无法解决问题，可以尝试重启计算机或者联系技术支持寻求帮助。

相关问题

RuntimeError: CUDA error: CUDA-capable device(s) is/are busy or unavailable

This error message indicates that the CUDA-capable device (such as a GPU) is currently being used by another process or is unavailable for some other reason. This can occur when trying to run a CUDA-enabled application or script on a system with limited GPU resources, or when multiple processes are trying to use the same GPU simultaneously.

To resolve this issue, you can try the following steps:

1. Check if another process is using the GPU: Use tools like nvidia-smi to check if there are any active processes that are using the GPU. If so, try stopping those processes and then run your application again.

2. Check if the GPU is available: Make sure that the GPU is properly connected and powered on, and that there are no hardware issues that could be preventing it from being used.

3. Reduce the workload: If you are running a workload that is too demanding for the GPU, consider reducing the workload to free up resources for other processes.

4. Use a different GPU: If you have multiple GPUs on your system, try using a different one to see if that helps resolve the issue.

5. Restart the system: Sometimes, restarting the system can help clear any issues that may be preventing the GPU from being used.

If none of these steps work, you may need to consult the documentation or support resources for the application or script you are trying to run, or contact the GPU manufacturer for further assistance.

RuntimeError: CUDA error: device-side assert triggered

This error occurs when a CUDA device-side assert is triggered, indicating that an assertion in the CUDA code has failed. This can happen for several reasons, such as invalid input data or a programming error in the CUDA code.

To debug this error, you can try the following steps:
1. Check the input data to make sure it is valid and within the expected range.
2. Verify that the CUDA code is correct and does not contain any programming errors.
3. Enable CUDA error checking by adding the following code at the beginning of your program:

import torch
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = True
torch.autograd.set_detect_anomaly(True)

4. If the error persists, try running the code on a different CUDA-enabled device to see if the issue is specific to the current device.

If you are unable to resolve the error, you can seek help from the CUDA community or the library or framework documentation you are using.