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The parameters of the network are changed to minimize the loss on the mini-batch, but usually the loss on the mini-batch is just the (weighted) sum of losses on each datum individually. Loosely, I would represent this as $$ dT = \frac{1}{\text{batch_size}} \sum_{i \in \text{batch}} dT_i$$

Where $dT$ is the update of the net parameters for the batch and $dT_i$ is only for one training example. Why can't $dT$ be calculated 'on-line' then, where the only RAM needed is on the partial sum for $dT$ and whichever $dT_i$ you are working on at that moment?

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Something similar to what you describe is frequently used in some domains and it is called gradient accumulation. In layman terms, it consists of computing the gradients for several batches without updating the weight and, after N batches, you aggregate the gradients and apply the weight update.

This certainly allows using batch sizes greater than the size of the GPU ram.

The limitation to this is that at least one training sample must fit in the GPU memory. If this is not the case, other techniques like gradient checkpointing can be used.

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  • $\begingroup$ Thanik you, now I have something to overcome my problem. Out of curiosity, could you give me an example of a problem where gradient accumulation can't be used for arbitrarily large batch sizes? Like possibly the gradients for the data are not independent. $\endgroup$ – basket Jul 17 '20 at 7:57
  • $\begingroup$ I have not seen cases where the gradients of different samples are not independent. If a single sample fits the GPU memory, I don't see why gradient accumulation would pose a limit in the batch size, provided you accumulate and apply gradients on the CPU memory. $\endgroup$ – noe Jul 17 '20 at 8:12

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