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AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
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.
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a memory-communication balanced strategy set Partial Redundancy Optimizer (PaRO).
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