Model training test optimization and deployment method and device based on container

Abstract

The invention provides a model training test optimization and deployment method and device based on a container. The method comprises the following steps of deploying service grid service for a container environment where model training is located; receiving an interface service request input parameter through a model training and testing algorithm driving program to carry out algorithm iteration;and in a model training and test algorithm iteration process, recording an association relationship between algorithm iteration parameters and a training target function, and recording and visually displaying an intermediate result in the training process. According to the method, the security is improved by using a container service grid conversion mode, the request of the algorithm serves a request interface, the problem that an original system cannot be embedded is solved, and meanwhile, the corresponding relationship between the model training parameters and the training target function in the training process is analyzed and stored, so that the model training intermediate process is visually displayed, and debugging and training optimization are facilitated.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a container-based model training test optimization and deployment method and a container-based model training test optimization and deployment device. Background technique [0002] Currently, the container network matching and conversion method provides a convenient and flexible calling method for driving algorithm training and testing programs through interface services outside the container. The current container algorithm debugging is mainly performed by jupyter notebook remote calling. The security and the compatibility of the original system are unsatisfactory. In addition, the current correspondence between algorithm training parameters and model evaluation capabilities mainly depends on the developer's own records, and it is not easy to trace the parameter adjustment and optimization process of model training. Contents of the invention [0003] In order to sol...

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Problems solved by technology

[0003] In order to solve the above technical problems, the present invention provides a container-based model training test tuning and deployment method, which can improve the security by using the container service grid conversion method, and the request of the algorithm is served by the request interface, without There will be a problem that it cannot be embedded in the original system. At the same time, the corresponding relationship between the parameters of model training and the training objective function in the training process is analyzed and stored, which is convenient for reverse tuning of model parameters according to the performance indicators of the model. In addition, the intermediate process of model training Visual display for easy debugging and training optimization

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