Abstract: A dynamic hashing apparatus includes a processor configured to obtain a target segment based on at least a part of a key value of an insertion request, compute a hash value by applying a remapping function corresponding to the obtained target segment to an input value based on the key value, and update the remapping function by adjusting a slope of the remapping function in response to the key value not being insertable into a target bucket selected based on at least a part of the computed hash value from among buckets included by the target segment.

Abstract: Disclosed is a training system performing training on a plurality of neural network models in parallel. The training system includes a first job proxy that receives a training request for a first neural network model and partitions a first training job corresponding to the first neural network model into first microservices, a second job proxy that receives a training request for a second neural network model and partitions a second training job corresponding to the second neural network model into second microservices, a scheduler that dynamically schedules the first microservices and the second microservices so as to be executed by heterogeneous processing units, a cluster that includes the heterogeneous processing units, sequentially executes the first microservices and sequentially executes the second microservices, and a distributed in-memory database that stores parameters generated in response to the execution of the first microservices and the second micro services.