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Recent years have witnessed a growing list of systems for distributeddata-parallel training. Existing systems largely fit into two paradigms, i.e.,parameter server and MPI-style collective operations. On the algorithmic side,researchers have proposed a wide range of techniques to lower the communicationvia system relaxations: quantization, decentralization, and communicationdelay. However, most, if not all, existing systems only rely on standardsynchronous and asynchronous stochastic gradient (SG) based optimization,therefore, cannot take advantage of all possible optimizations that the machinelearning community has been developing recently. Given this emerging gapbetween the current landscapes of systems and theory, we build BAGUA, aMPI-style communication library, providing a collection of primitives, that isboth flexible and modular to support state-of-the-art system relaxationtechniques of distributed training. Powered by this design, BAGUA has a greatability to implement and extend various state-of-the-art distributed learningalgorithms. In a production cluster with up to 16 machines (128 GPUs), BAGUAcan outperform PyTorch-DDP, Horovod and BytePS in the end-to-end training timeby a significant margin (up to 2 times) across a diverse range of tasks.Moreover, we conduct a rigorous tradeoff exploration showing that differentalgorithms and system relaxations achieve the best performance over differentnetwork conditions.

BAGUA: Scaling up Distributed Learning with System Relaxations