OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 11.76 GiB total capacity; 11.58 GiB already allocated; 1.81 MiB free; 11.62 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
时间: 2024-02-17 20:27:19 浏览: 40
这个错误是由于在使用PyTorch时,尝试在GPU上分配的内存超过了可用的内存限制。这可能是由于模型或数据集过大导致的。您可以尝试以下几个方法来解决这个问题:
1. 减少批次大小(batch size)或缩小模型规模,以减少GPU内存的使用量。
2. 尝试使用更大的GPU或分布式训练。
3. 在训练之前清理GPU内存,可以使用PyTorch提供的torch.cuda.empty_cache()方法。
4. 尝试设置max_split_size_mb参数,以避免内存碎片化。具体的使用方法可以参考PyTorch官方文档中的Memory Management和PYTORCH_CUDA_ALLOC_CONF章节。
希望这些方法能够帮助您解决这个问题。
相关问题
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 2.00 GiB total capacity; 1.67 GiB a
vailable; 40.00 KiB already allocated; 19.94 MiB free; 4.00 MiB cached)
This error message indicates that the program tried to allocate 20.00 MiB of memory on the GPU, but there was not enough memory available. The GPU has a total capacity of 2.00 GiB, but only 1.67 GiB was available at the time. 40.00 KiB had already been allocated, leaving only 19.94 MiB free. Additionally, 4.00 MiB had been cached, which means that it was reserved for future use but not currently in use.
To resolve this issue, you can try one or more of the following:
1. Reduce the size of the input data or the size of the model being used. This will reduce the amount of memory required.
2. Increase the GPU memory capacity. If possible, add more memory to the GPU or switch to a GPU with a larger memory capacity.
3. Use a smaller batch size. This will reduce the amount of memory required for each iteration.
4. Use gradient checkpointing. This technique allows the model to compute gradients for small subsets of parameters at a time, reducing the amount of memory required.
5. Use mixed precision training. This technique allows the model to use 16-bit floating point numbers instead of 32-bit, reducing the amount of memory required.
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 148.00 MiB (GPU 0; 4.00 GiB total capacity; 5.23 GiB already allocated;
torch.cuda.OutOfMemoryError是指在使用PyTorch时,尝试在CUDA显存中分配内存时出现错误,因为显存已经被其他操作占用完毕。其中引用和引用提到了相同的错误信息和可能的解决方法。根据这些引用内容,可以推测解决此错误的方法是通过设置max_split_size_mb参数来避免内存碎片化。你可以参考PyTorch的Memory Management和PYTORCH_CUDA_ALLOC_CONF文档了解更多的信息。引用也提到了类似的错误信息,但给出了不同的显存容量和已分配内存的数值。这说明出现该错误的具体原因可能因系统配置不同而有所不同。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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