To combine stochastic image augmentation with distributed deep learning, a unified training function can be implemented to orchestrate the entire pipeline. A function like train_with_data_aug accepts specific augmentation transformation sequences for both the training and testing datasets. It utilizes data loaders to apply these random transformations dynamically and construct minibatches. The function then configures an optimization algorithm, such as Adam, and passes the neural network, data iterators, loss function, and optimizer to a concise multi-GPU training loop. This approach seamlessly integrates complex data preprocessing with high-performance distributed training across all available hardware.