Description: Gradient accumulation is a technique used in training deep learning models, especially in various types of neural networks. Its main purpose is to simulate a larger batch size by accumulating gradients over several iterations before updating the model parameters. This is particularly useful in situations where memory is limited, as it allows for processing more data without increasing the batch size at each iteration. Instead of updating the model weights after each sample or small batch, gradients are summed over a specific number of steps, and only after this process is an update performed. This technique not only helps reduce memory usage but can also improve training stability and model convergence, as it relies on a more representative average of gradients. Gradient accumulation is especially relevant in the context of training deep learning models with long and complex data sequences, where efficient memory management is crucial for performance. In summary, gradient accumulation is an effective strategy for optimizing the training of deep learning models, allowing for more efficient use of computational resources.
