CUDA out of memory

CUDA out of memory error occurs when the GPU does not have enough memory to store all the data required for a specific CUDA operation. This error can occur when the data being processed is too large for the memory available on the GPU.

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

1. Reduce the batch size: You can try to reduce the batch size of the data being processed. This can help to reduce the memory requirements for the operation.

2. Use smaller models: You can try to use smaller models with fewer parameters. This can help to reduce the memory requirements for the operation.

3. Use mixed precision training: You can try to use mixed precision training, which uses lower precision data types for certain parts of the training process. This can help to reduce the memory requirements for the operation.

4. Use gradient checkpointing: You can try to use gradient checkpointing, which stores only a portion of the computation graph at a time, reducing the memory requirements for the operation.

5. Increase the GPU memory: You can try to increase the GPU memory by using a GPU with more memory or by using multiple GPUs to distribute the workload.