Cross-machine learning platform model definition protocol and adaptation system

Abstract

The invention provides a cross-machine learning platform model definition protocol and configuration system, and relates to the field of artificial intelligence automatic machine learning. The systemcomprises a computational graph construction engine; a computational graph optimization engine; a cross-machine learning platform model definition protocol (Cross-Platforms Model definition Protocol);a computational graph encoding engine; a hyper-parameter recommendation engine; a protocol parsing engine; a model training process control assembly; a hardware information detection and configuration system. The system has the advantages that cross-platform machine learning network construction is achieved, the system is easy to use, and the machine learning maintenance threshold is lowered; machine learning network construction in a dragging mode is achieved, and the latest algorithm network can be iterated rapidly; a large number of algorithm models are provided, and machine learning and deep learning network construction under different scenes are supported; a parameter recommendation function is provided, and a lot of parameter adjustment time is saved; algorithm network constructionfrom data cleaning to model release full life cycle is provided, and the system can be directly put into production and use.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and automatic machine learning, and specifically relates to a cross-machine learning platform model definition protocol and configuration system Background technique [0002] With the arrival of a new wave of artificial intelligence in recent years, machine learning related technologies have been applied to many industries and fields. Nowadays, a variety of mainstream machine learning frameworks can efficiently and quickly build machine learning learning networks, but machine learning algorithms written in different frameworks cannot achieve the expected functions on other frameworks. At the same time, deploying different frameworks also requires switching back and forth between different frameworks. [0003] The use of different frameworks also requires technicians to be very familiar with the syntax of different frameworks. However, machine learning must be deeply integrated with industry...

AI Technical Summary

Problems solved by technology

[0003] The use of different frameworks also requires technicians to be very familiar with the syntax of different frameworks. However, machine learning must be deeply integrated with industry business to maximize the value of machine learning. Therefore, the construction of machine learning networks expressed in code form is very important for front-line businesses. Users are not friendly

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