Offline batch inference is a common task in the industry for deep learning
applications, but it can be challenging to ensure stability and performance
when dealing with large amounts of data and complicated inference pipelines.
This paper demonstrated AntBatchInfer, an elastic batch inference framework,
which is specially optimized for the non-dedicated cluster. AntBatchInfer
addresses these challenges by providing multi-level fault-tolerant
capabilities, enabling the stable execution of versatile and long-running
inference tasks. It also improves inference efficiency by pipelining,
intra-node, and inter-node scaling. It further optimizes the performance in
complicated multiple-model batch inference scenarios. Through extensive
experiments and real-world statistics, we demonstrate the superiority of our
framework in terms of stability and efficiency. In the experiment, it
outperforms the baseline by at least $2\times$ and $6\times$ in the
single-model or multiple-model batch inference. Also, it is widely used at Ant
Group, with thousands of daily jobs from various scenarios, including DLRM, CV,
and NLP, which proves its practicability in the industry.

AntBatchInfer 是一种针对非专用集群进行优化的弹性离线批量推理框架，通过提供多级容错能力和改进推理效率的方法，稳定高效地执行复杂的单模型和多模型批量推理任务，并在稳定性和效率方面表现优越。