Artificial intelligent platform system based on deep learning

Abstract

The invention discloses an artificial intelligent platform system based on deep learning. The artificial intelligent platform system comprises a platform layer, a model layer and an application layer;the platform layer is used for permission managing, distributed storing, CPU computing resource managing, distributed computing and training and task scheduling; the model layer is used for providinga machine learning model and a deep learning model; and the application layer is used for resource managing and monitoring, model defining and training, interactive programming environment providing,intelligent data labeling and model deriving and publishing. The AI platform system is developed through an engineering means to increase the utilization rate of hardware resources of a GPU and the like, reduce the hardware input cost, help an algorithm engineer to more conveniently apply various deep learning technologies to free the algorithm engineer from tedious environment operation and maintenance, provide efficient storing of massive training data and isolate user resources, and therefore access permission control is more secure; training data and training tasks are managed in a unified mode, and the machine learning process is standardized and processed; and data labeling is automated, and the model iteration efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, in particular to an artificial intelligence platform system based on deep learning. Background technique [0002] In recent years, with the rapid development of artificial intelligence technology, deep learning has swept every corner of IT, changing the model of algorithm research and software development in various fields, and also bringing great benefits to IT infrastructure construction and platform tool development. Here comes a new requirement. Quickly build a distributed deep learning training platform and accelerate the training of deep neural networks, which can effectively improve the company's competitiveness. [0003] Currently, there are many deep learning frameworks, such as mxnet, tensorflow, cntk, etc., which are familiar to everyone. These frameworks are developed in different languages ​​and have different interface designs. many difficulties. [0004] One of t...

AI Technical Summary

Problems solved by technology

[0007] 1. It does not support GPU resource scheduling management. Under the load of deep learning, GPU is a first-class citizen of resource scheduling, and GPU resources cannot be used, which has a great impact on the efficiency of model training;

[0008] 2. It only supports data storage, lacks support for data labeling, and needs to seek additional labeling tools;

[0009] 3. There is no authority control system, and data security cannot be guaranteed;

[0010] 4. There is no interactive environment, and the development efficiency of algorithm engineers is low

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