What does "Data parallelism in distributed training" imply?

Data parallelism in distributed training refers to the approach where the same model architecture and computations are executed across multiple devices, but each device operates on different subsets of the training data. This method allows for efficient utilization of hardware resources, as it divides the workload of training a model into smaller chunks that can be processed simultaneously.

By employing data parallelism, the total training dataset is split into batches that are distributed across different machines or GPUs. Each device processes its own batch independently, calculating the gradients based on the loss computed from the input data it has. After processing, the gradients are usually aggregated to update the model weights, ensuring that all devices contribute to the same model training progression.

This methodology is particularly advantageous for scaling the training of large models or datasets since it allows for faster processing times by leveraging parallel computing. It aligns with the principle of distributed computing, where multiple agents work together to process data more efficiently than a single device could.