CPU (central processing unit)+GPU (graphic processing unit) cluster management method, device and equipment for implementing target detection

Abstract

The invention discloses a CPU (central processing unit)+GPU (graphic processing unit) cluster management method for implementing target detection. The method includes the steps: performing interface configuration on various framework model algorithms in the field of collected target detection to obtain a runnable framework model algorithm; packing the runnable framework model algorithm by a dockertechnology to obtain a corresponding docker packet; calling the corresponding docker packet according to the runnable framework model algorithm corresponding to user data, and scheduling the docker packet to generate a corresponding testing environment. By the method, dynamic management can be realized to transfer resources, resource use ratio and working efficiency are greatly increased, use cost is further reduced, and research and development time is shortened. The invention further discloses a CPU+GPU cluster management device and equipment for implementing target detection and a computerreadable storage medium which have the advantages.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a CPU+GPU cluster management method for realizing target detection, and also relates to a CPU+GPU cluster management device, equipment and computer-readable storage medium for realizing target detection. Background technique [0002] Computer vision is the use of computers to realize human visual functions, and its research goal is to enable computers to have the ability to recognize the surrounding environment through one or more images. As a branch of computer vision technology, target detection is to observe and classify targets in the field of view, such as people or vehicles, and then analyze their behavior. At present, many scientific research institutions have invested a lot of manpower and material resources in the field of target detection, especially in the field of intelligent monitoring, and some of their achievements have been put into the market, but the...

AI Technical Summary

Problems solved by technology

However, due to the large memory usage, when individuals download and configure the environment, it takes several weeks at least and several months at most, and there are too many related model frameworks for deep learning of target detection, and users are easy to get lost, which further increases the time cost. ;At the same time, the current system has 8GPU cards on a single machine. If one person is running, only 1 card is used, which will cause waste of resources, and if multiple people log in at the same time, it will cause resource conflicts, resulting in lower resource utilization. rate; in addition, because the system cannot perform visual monitoring or real-time analysis, it is not easy to perform wrong positioning, resulting in a reduction in work efficiency

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