Graph model task implementation method and system supporting multi-engine framework

Abstract

The embodiment of the invention provides a graph model task implementation method and system supporting a multi-engine framework, and the method comprises a graph model task processing method and a graph model task deployment method, and the task processing method comprises the steps: obtaining graph data in a preset data format; determining a target machine learning engine or a target graph learning framework from more than two machine learning engines and / or more than two graph learning frameworks; converting the graph data from the preset data format into a data format corresponding to the target machine learning engine or the target graph learning framework to obtain target input data; and providing the target input data to the target machine learning engine or the target graph learning framework, and processing the target input data based on a supported machine learning model through the target machine learning engine or the target graph learning framework to realize a graph model task.

Description

technical field [0001] The present application relates to the technical field of machine learning, and in particular, to a method and system for implementing graph model tasks. Background technique [0002] With the development of computer technology, machine learning related technologies have been applied to various fields, and occupy an important position in data processing or data analysis in various fields. The machine learning engine can provide scalable computing structure components in the field of machine learning, and can realize various data processing tasks in the field of machine learning. For example, in the related technical fields of graph data such as knowledge graphs, data processing tasks such as model training and model prediction of graph models can be realized through machine learning engines; for example, graph learning frameworks can also be constructed based on machine learning engines, so that graph learning The framework implements data processing ...

AI Technical Summary

Problems solved by technology

Different machine learning engines and graph learning frameworks have their own characteristics and advantages, but their respective input data formats, model codes, model training, and task deployment methods are quite different, which brings great complexity to business applications. sex

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