A concise multi-GPU training loop leverages high-level framework abstractions to orchestrate data-parallel execution. The training function begins by wrapping the neural network in a distributed execution module (such as nn.DataParallel in PyTorch) and moving it to the primary processing device. During each epoch, it iterates over the training minibatches, calling the corresponding minibatch training function to perform the synchronized forward and backward passes. It accumulates the training loss and accuracy, and periodically evaluates the model on the test dataset using multiple GPUs, tracking performance metrics efficiently without manually managing individual device state aggregations.