Architecture of the software development and testing platform neural network models for creating specialized dictionaries

The authors propose the implementation of a software platform for creating neural network models with their testing, used to create specialized dictionaries for automated systems. The software platform allows speeding up the process of finding the optimal method for creating a neural network model. The platform is based on an overview of existing tools and methods used to create clock analysis models and software virtualization technologies. A research result is the proposed architecture of a software platform for creating specialized dictionaries that ensures the simultaneous creation of different neural network models in virtual containers. A container virtualization of software elements that create and test neural network models provides all mathematical calculations for processing text-based information; decentralized, in parallel and isolated training and testing a neural network model. The data exchange between virtual containers, as well as the storage of all the results of the container's operation occurs through a special data bus, which is disk space that all containers have access to. The use of the developed platform can speed up the process of searching for an algorithm for creating specialized dictionaries through testing various hypotheses based on various methods for constructing models. The process acceleration occurs due to the parallelism and reuse of the mathematical results of the general stages of algorithms whose mathematical calculations were carried out by a similar algorithm. This allows scaling and splitting the learning process not only through the parallel crea-tion of various models, but also at the level of individual model creation stages. The proposed platform was successfully used to find a locally optimal method for creating a model in highly specialized limited-field texts.