Distributed training is a method of training neural networks across multiple devices or machines to speed up the learning process and handle large datasets. Instead of training on a single machine, the work is divided among multiple processors, which each handle parts of the model or data.
Techniques like data parallelism, where different machines process different batches of data, or model parallelism, where the model is split across devices, allow large-scale models to be trained more efficiently. Frameworks like TensorFlow and PyTorch support distributed training.
Distributed training is essential for deep learning tasks that involve large datasets or complex models, such as those used in image processing or natural language understanding, where training time and resource consumption are significant.