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How to solve ' CUDA out of memory. Tried to allocate xxx MiB' in pytorch?
Written by- Aionlinecourse725 times views
If you are seeing the 'CUDA out of memory' error in PyTorch, it means that your GPU does not have sufficient memory to complete the operation you are trying to perform. Here are a few things you can try to resolve this issue:
1.Reduce the batch size: One of the main causes of the 'CUDA out of memory' error is a large batch size. Try reducing the batch size and see if it resolves the issue.
2. Increase the GPU memory: If you have access to the GPU settings, you can try increasing the GPU memory allocated to PyTorch. This can be done through the CUDA_VISIBLE_DEVICES environment variable.
3. Use gradient accumulation: Gradient accumulation is a technique that allows you to break up a large batch into smaller batches and perform the forward and backward passes separately. This can help reduce the amount of memory required by your model.
4. Use a smaller model: If your model is too large to fit in the GPU memory, you can try using a smaller model or pruning unnecessary weights.
5. Use half precision: PyTorch supports half precision (fp16) computations, which can significantly reduce the memory footprint of your model. You can try using fp16 tensors and see if it helps.
6. Use memory profiling: PyTorch has a built-in memory profiler that can help you identify which parts of your model are using the most memory. You can use this information to optimize your model and reduce its memory usage.
I hope these suggestions help! If you have any further questions or need more guidance, don't hesitate to ask.
1.Reduce the batch size: One of the main causes of the 'CUDA out of memory' error is a large batch size. Try reducing the batch size and see if it resolves the issue.
2. Increase the GPU memory: If you have access to the GPU settings, you can try increasing the GPU memory allocated to PyTorch. This can be done through the CUDA_VISIBLE_DEVICES environment variable.
3. Use gradient accumulation: Gradient accumulation is a technique that allows you to break up a large batch into smaller batches and perform the forward and backward passes separately. This can help reduce the amount of memory required by your model.
4. Use a smaller model: If your model is too large to fit in the GPU memory, you can try using a smaller model or pruning unnecessary weights.
5. Use half precision: PyTorch supports half precision (fp16) computations, which can significantly reduce the memory footprint of your model. You can try using fp16 tensors and see if it helps.
6. Use memory profiling: PyTorch has a built-in memory profiler that can help you identify which parts of your model are using the most memory. You can use this information to optimize your model and reduce its memory usage.
I hope these suggestions help! If you have any further questions or need more guidance, don't hesitate to ask.