Method and device for generating and applying deep learning model based on deep learning framework

Abstract

The invention provides a method and device for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. According to the specific implementation scheme, a basic operation environment is constructed on target equipment, and the basic operation environment provides environment preparation for the overall generation process of a deep learning model; according to the business demand and / or the hardware demand, generating a basic function of the deep learning model in the basic operation environment, and obtaining a first processing result; generating an extended function of the deep learning model in the basic operation environment based on the first processing result, and obtaining a second processing result; and performing function test on the second processing result by utilizing a preset test script, and outputting a test result. The technical problem that the overall process of model design cannot be set during model construction in the prior art is solved. Wherein the function test on the second processing result by using the preset test script is used for ensuring the reliability of the whole process of the model design.

Description

technical field [0001] The present disclosure relates to the field of computer technology, in particular to a method and device for generating and applying a deep learning model based on a deep learning framework. Background technique [0002] With the development of artificial intelligence technology, more and more companies use deep learning frameworks to develop deep learning models to solve problems in their respective business scenarios. The typical process is: the business demander proposes requirements according to their own business scenarios, the algorithm engineer selects the appropriate basic algorithm, prepares appropriate data, and uses the deep learning framework to train a series of actions such as data processing, algorithm optimization, parameter optimization, and model training. The deep learning model that meets the requirements is deployed in the actual business production environment, and finally the business goes online. [0003] However, when using th...

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Problems solved by technology

[0003] However, when using the above solution to solve the problem, the manual processing process is too cumbersome, and the algorithm obtained after manual processing may have the problem of not being suitable for the business environment

[0004] For the above problems, no effective solution has been proposed

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