RuntimeError: CUDA out of memory. Tried to allocate 256.00 MiB
时间: 2023-10-23 17:42:58 浏览: 49
This error message indicates that the program you are running has attempted to allocate more memory on the GPU than is currently available.
To resolve this issue, you can try one or more of the following solutions:
1. Reduce the batch size of your training data to decrease the memory usage.
2. Reduce the size of your model by removing unnecessary layers or reducing the number of parameters.
3. Use a GPU with more memory capacity or use multiple GPUs to distribute the workload.
4. Use mixed precision training, which can reduce the memory usage by using lower-precision data types for some calculations.
5. Clear the GPU memory by restarting the kernel or resetting the GPU.
相关问题
RuntimeError: CUDA out of memory. Tried to allocate 240.00 MiB
当出现"RuntimeError: CUDA out of memory. Tried to allocate 240.00 MiB"错误时,这通常是由于GPU显存不足导致的。解决这个问题的一种方法是通过减小batch_size来降低内存需求。另外,使用item()属性来获取torch变量的标量值也可以节省内存。在测试阶段,可以添加with torch.no_grad()的上下文管理器来减少内存的使用。此外,还可以通过释放内存的方法来解决问题。
RuntimeError: CUDA out of memory. Tried to allocate 14.00 MiB
这个错误是由于CUDA内存不足导致的。根据引用\[1\]和引用\[2\]的信息,你的GPU总容量为4.00 GiB或10.76 GiB,但已经分配了2.34 GiB或1.82 GiB的内存,剩余的内存不足以分配14.00 MiB的内存。这可能是由于你的模型或数据的规模过大,导致内存不足。你可以尝试减小batch size或者使用更小的模型来减少内存的使用。另外,你还可以尝试设置max_split_size_mb参数来避免内存碎片化。关于内存管理和PYTORCH_CUDA_ALLOC_CONF的更多信息,请参考PyTorch的文档。
此外,根据引用\[3\]的信息,你还可以通过手动杀死占用GPU内存的进程来释放内存。你可以使用kill命令加上进程的PID来终止该进程,例如kill -9 31272。
综上所述,你可以通过减小batch size、使用更小的模型、设置max_split_size_mb参数或手动杀死占用内存的进程来解决CUDA内存不足的问题。
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