NLP model training release identification system

Abstract

The invention discloses an NLP model training release identification system. The system comprises at least two GPU servers; an NLP recognition model; an NLP semanteme identification service process; at least one NLP gateway; at least two GPU server resource scheduling instruction actuators, wherein the GPU server resource scheduling instruction actuators are used for executing the scheduling instruction initiated by the resource scheduling center module; at least one resource scheduling center module which is used for allocating GPU server resources and coordinating an executor to execute an instruction in the following process, and comprises training of an NLP recognition model, release of the NLP recognition model and synchronous change of a relationship between the NLP recognition modeland service data; and at least one service registration center module which is used for recording and deleting the service information from the registration center while the NLP semanteme identifiesthe start and stop of the service process, and is used for the NLP gateway to self-discover the service process.

Description

technical field [0001] The invention relates to the field of model training, in particular to an NLP model training release recognition system. Background technique [0002] NLP (Natural Language Processing) is a subfield of artificial intelligence (AI). At present, conventional NLP needs to go through three processes of model training, publishing, and starting the model before it can provide normal interface services. These three Process At present, the industry requires developers to manually operate on the server. Every time the NLP semantic recognition model is released and trained, there is a risk of misoperation, and it is easy to cause failure due to human operation errors. [0003] In addition, after the NLP semantic recognition service process is started, it needs to be manually configured. For example, to provide a service call interface externally, it is necessary to configure the relationship between the nginx service interface route, the NLP recognition model an...

AI Technical Summary

Problems solved by technology

[0002] NLP (Natural Language Processing) is a subfield of artificial intelligence (AI). At present, conventional NLP needs to go through three processes of model training, publishing, and starting the model before it can provide normal interface services. These three Process At present, the industry requires developers to manually operate on the server. Every time the NLP semantic recognition model is released and trained, there will be a risk of misoperation, and it is easy to cause failure due to human operation errors.

[0003] In addition, after the NLP semantic recognition service process is started, it needs to be manually configured. For example, to provide a service call interface to the outside world, it is necessary to configure the relationship between the nginx service interface route, the NLP recognition model and the service process. The process is cumbersome and not suitable for daily operation and maintenance. Has a certain mechanical repeatability

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