Method for constructing multi-task computer vision application service based on deep learning

Abstract

The invention discloses a method for constructing a multi-task computer vision application service based on deep learning, and belongs to the technical field of artificial intelligence. The method forconstructing the multi-task computer vision application service based on deep learning comprises the following steps: S1, defining a data input data source format of the application service; s2, defining GPU resource scheduling rules are defined so that GPU resources are automatically allocated as required; s3, defining a service internal bus; s4, defining a model arrangement pipeline; and S5, containerized deployment and operation and maintenance of the application service. According to the method for constructing the multi-task computer vision application service based on deep learning, theimplementation difficulty of deep learning can be reduced, the development efficiency is improved, meanwhile, the development cost is reduced, and good application and popularization value is achieved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, and specifically provides a method for constructing multi-task computer vision application services based on deep learning. Background technique [0002] At present, the development of artificial intelligence has been fully concerned and promoted with the help of breakthroughs in deep learning technology. Governments of various countries attach great importance to it, and all walks of life have reached a consensus that it has become a development hotspot. Deep learning is the key technology of this round of artificial intelligence explosion. The breakthroughs made by artificial intelligence technology in the fields of computer vision and natural language processing have ushered in a new round of explosive development of artificial intelligence. And deep learning is the key technology to achieve these breakthroughs. The deep learning technology is still not perfect, the applicati...

AI Technical Summary

Problems solved by technology

The deep learning technology is still not perfect, the application threshold is high, the algorithm modeling and parameter adjustment process is complex and cumbersome, the algorithm design cycle is long, the system implementation and maintenance are difficult, and the service is difficult to deploy, etc.

Getting started with deep learning can be time-consuming and cumbersome, even for seasoned deep learning practitioners