Gradient accumulation is a technique used in training deep learning models where gradients are computed over multiple mini-batches before updating the model's weights. This approach helps manage memory constraints and improves training efficiency by allowing larger effective batch sizes without increasing the actual memory footprint of the training process. It plays a significant role in distributed training and data parallelism, where gradients can be aggregated from multiple devices before performing a single update.
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