Summary of Antbatchinfer: Elastic Batch Inference in the Kubernetes Cluster, by Siyuan Li et al.
AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
by Siyuan Li, Youshao Xiao, Fanzhuang Meng, Lin Ju, Lei Liang, Lin Wang, Jun Zhou
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AntBatchInfer framework efficiently handles offline batch inference tasks on non-dedicated clusters. It addresses challenges through multi-level fault-tolerance, pipelining, intra-node and inter-node scaling, and optimized performance for complicated multiple-model scenarios. The framework demonstrates significant improvements in stability and efficiency, outperforming baseline methods by at least 2x and 6x in single- or multi-model inference tasks. AntBatchInfer is widely adopted at Ant Group, processing thousands of daily jobs across various applications like DLRM, CV, and NLP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AntBatchInfer is a special kind of computer program that helps big companies process lots of data quickly and accurately. It’s good at handling problems when things go wrong, which makes it very reliable. This tool also makes sure the processing happens fast and efficiently, even when there are many different models involved. In tests, AntBatchInfer was much better than other methods, doing tasks up to 6 times faster! Many companies use this tool every day for things like image recognition and natural language processing. |
Keywords
» Artificial intelligence » Inference » Natural language processing » Nlp
