Device and method for providing benchmark result of artificial intelligence based model

A computer program stored in a non-transitory computer readable medium, wherein the computer program allows a first computing device to perform following operations to generate a benchmark result when executed by the first computing device, and wherein the operations comprise:

• receiving, from a second computing device comprising a plurality of modules which perform different operations related to an artificial intelligence-based model, module identification information indicating which module among the plurality of modules of the second computing device triggers a benchmark operation of the first computing device;
• and
• providing, to the second computing device, a benchmark result based on the module identification information, and
• wherein providing, to the second computing device, a benchmark result based on the module identification information comprises:
• if the module identification information indicates a first module, i) providing performance information generated by executing an inputted model at one or more nodes, to the second computing device, or ii) providing, to the second computing device, a benchmark result for determining a target node at which a converted training model or a training model corresponding to an inputted dataset is to be executed, by executing the converted training model or the training model corresponding to the inputted dataset at the one or more nodes;
• and
• if the module identification information indicates a second module, i) providing performance information corresponding to an entirety of the inputted model and providing performance information in units of blocks of the inputted model to the second computing device, by executing the inputted model at one or more nodes, wherein the performance information in units of blocks of the inputted model is used to optimize or compress in units of blocks of the inputted model, or ii) providing, to the second computing device, a benchmark result comprising configuration data used for generating a compressed model corresponding to the inputted model, by executing the compressed model corresponding to the inputted model at the one or more nodes.

A first computing device for generating a benchmark result, comprising:

• at least one processor;
• and
• a memory,
• wherein the at least one processor:
• receives, from a second computing device comprising a plurality of modules which perform different operations related to an artificial intelligence-based model, module identification information indicating which module among the plurality of modules of the second computing device triggers a benchmark operation of the first computing device;
• and
• provides, to the second computing device, a benchmark result based on the module identification information, and
• wherein providing, to the second computing device, a benchmark result based on the module identification information comprises:
• if the module identification information indicates a first module, i) providing performance information generated by executing an inputted model at one or more nodes, to the second computing device, or ii) providing, to the second computing device, a benchmark result for determining a target node at which a converted training model or a training model corresponding to an inputted dataset is to be executed, by executing the converted training model or the training model corresponding to the inputted dataset at the one or more nodes;
• and
• if the module identification information indicates a second module, i) providing performance information corresponding to an entirety of the inputted model and providing performance information in units of blocks of the inputted model to the second computing device, by executing the inputted model at one or more nodes, wherein the performance information in units of blocks of the inputted model is used to optimize or compress in units of blocks of the inputted model, or ii) providing, to the second computing device, a benchmark result comprising configuration data used for generating a compressed model corresponding to the inputted model, by executing the compressed model corresponding to the inputted model at the one or more nodes.