This paper introduces GPipe, a model-parallelism library designed to train large neural networks efficiently using pipeline parallelism. It partitions models across accelerators, processes micro-batches in parallel, and supports synchronous gradient updates. GPipe enables near-linear scaling with the number of devices while maintaining model quality and training stability. It achieves state-of-the-art performance in large-scale image classification (AmoebaNet) and multilingual machine translation (6B parameter Transformer), demonstrating flexibility across tasks. Its impact lies in making massive model training more practical and accessible across diverse architectures without relying on high-speed interconnects or custom model designs.
Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks. To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models.
