Distributed Training

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Distributed training is a machine learning technique where a single model is trained by multiple devices working together and constantly sharing updates. Each device processes part of the training data or part of the model, then synchronizes its results with the others so the model stays consistent. This coordination step is what makes distributed training different from general parallel computing.

The main purpose of distributed training is to train very large models or reduce training time when one machine isn’t enough. Because all devices must exchange information during training, performance depends not only on computing power but also on how efficiently updates are shared. When designed well, distributed training makes it possible to train models that would be too slow or too large to handle on a single machine, while still producing one unified model at the end.

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