Query-based database linkage distributed deep learning system, and method therefor

A deep learning framework application database server comprising:

• an input/output unit configured to receive an inference query from a user;
• a storage unit serving as a database and comprising a previously learned first learning model table and an inference dataset table associated with the inference query;
• and
• a framework unit configured to interwork with the database and perform deep learning for the inference query using the first learning model table and the inference dataset table,
• wherein a first learning model linked to the first learning model table comprises multiple tasks, the multiple tasks correspond to a plurality of rows in a network table included in the first learning model table, each of the multiple tasks has a unique number respectively corresponding to row numbers in the network table, the deep learning framework application database server further comprises a control unit configured to control a first distributed server among a plurality of distributed servers to execute a sequence of first to second tasks among the multiple tasks, enabling distributed deep learning inference, each server of the plurality of distributed servers comprises a deep learning framework that interworks with the database and comprises the first learning model table,
• wherein a learning model management module is configured to select a second learning model table suitable for a function of the inference query in an absence of the first learning model table,
• the second learning model table is converted into the first learning model table through deep learning training for a first detailed function based on an associated training dataset table, and
• the deep learning training is performed on the plurality of distributed servers in a distributed manner,
• wherein the control unit is configured to spread a batch size of a training dataset, the second learning model table, and the training dataset table to the plurality of distributed servers,
• the deep learning framework application database server is configured to function as a first distributed server among the plurality of distributed servers,
• the control unit is configured to randomly change a data order in the training dataset table, then divide data to the batch size, and convert divided data into a batch dataset table,
• the framework unit is configured to construct a model architecture using an architecture table belonging to the second learning model table, initialize a learning parameter table belonging to the second learning model table and then assign the initialized learning parameter table to the model architecture to generate a second learning model, and perform deep learning training on the second learning model by using multiple mini-batches in the batch dataset table,
• the framework unit is configured to derive a new learning parameter when batch learning on one mini-batch among the multiple mini-batches ends,
• the control unit is configured to spread the new learning parameter to remaining other distributed servers of the plurality of distributed servers,
• the control unit is configured to spread the learning parameters derived from the framework unit to other framework units, based on one of policies,
• such as
• an immediate sharing policy where as soon as each batch learning ends in a certain framework unit, its latest learning parameter is spread to the other framework units,
• a time period-based sharing policy where the latest learning parameter is spread to other framework units after a predetermined time period, and
• a learning cycle-based sharing policy where the latest learning parameter is spread after a predetermined number of batch learnings.