What could be the reason for the GPU running out of memory on a previously functional code?

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There are several possible reasons why a GPU could run out of memory on a previously functional code:

1. Increased complexity of the model - If the model has become more complex, then it could be using more memory on the GPU. This could be due to an increase in the number of layers or the use of larger filters.

2. Increase in batch size - If the batch size has been increased, then more data is being processed simultaneously, which can lead to an increase in memory usage.

3. Increase in the size of the input data - If the input data has increased in size, then the GPU may not have enough memory to hold all the data at once.

4. Insufficient memory on the GPU - If the GPU does not have enough memory to handle the model or data, then it will run out of memory.

5. Memory leakage - If there is a memory leak in the code, then the GPU memory could be quickly consumed, causing it to run out of memory.

6. Outdated drivers or firmware - If the drivers or firmware are outdated, they may not be able to properly allocate memory to the GPU, leading to memory issues.