Efficient Distributed Training via Dual Batch Sizes and Cyclic Progressive Learning

Distributed machine learning is critical for training deep learning models on large datasets and with numerous parameters. Current research primarily focuses on leveraging additional hardware resources and powerful computing units to accelerate the training process. As a result, larger batch sizes are often employed to speed up training. However, training with large batch sizes can lead to lower accuracy due to poor generalization. To address this issue, we propose the dual batch size learning scheme, a distributed training method built on the parameter server framework. This approach maximizes training efficiency by utilizing the largest batch size that the hardware can support while incorporating a smaller batch size to enhance model generalization. By using two different batch sizes simultaneously, this method reduces testing loss and enhances generalization, with minimal extra training time. Additionally, to mitigate the time overhead caused by dual batch size learning, we propose the cyclic progressive learning scheme. This technique gradually adjusts image resolution from low to high during training, significantly boosting training speed. By combining cyclic progressive learning with dual batch size learning, our hybrid approach improves both model generalization and training efficiency. Experimental results using ResNet-18 show that, compared to conventional training methods, our method can improve accuracy by 3.3% while reducing training time by 10.6% on CIFAR-100, and improve accuracy by 0.1% while reducing training time by 35.7% on ImageNet.