The application provides a configuration-driven DL4NLP model automatic training and deployment method, which comprises the following steps:
parsing a
YAML configuration file and verifying parameters to generate a
unified process configuration; automatically loading data and performing preprocessing according to the configuration to generate a model input
tensor; instantiating model components through a component factory and assembling the model components into a complete
network model; automatically configuring a training environment to
train and evaluate the model to obtain an optimal model; exporting the model into a TorchScript format binary file; and automatically generating a standardized deployment
package based on a template engine. The application abstracts the whole process of
model development and deployment into a configuration-driven standardized pipeline by
parsing the
YAML configuration file, automatically performs data loading and preprocessing, model component instantiation and
assembly, training optimization and deployment
package generation, and solves the problems of low development efficiency, long cycle and technical gap between training and deployment caused by repeated
code writing and manual conversion and deployment for different tasks in traditional model training and deployment.