  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.
