Whole-process artificial intelligence competition system based on local model and cloud feedback and data processing method thereof

Abstract

The invention aims at the characteristics of artificial intelligence, creatively provides a whole-process artificial intelligence competition system based on a local model and cloud feedback and a data processing method thereof. Through the technical scheme of the invention, pyTorch, tensorFloW, Keras, Scikit-Learn, caffe, MXNet, Theano, Torch and other frameworks for deep learning are operated and supported in local equipment (PC and the like) through a set of script commands and codes, thereby realizing automatic detection and installation of development environment dependence. Debugging ofsmall sample data is performed by using a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) of local equipment (PC and the like), codes are submitted through script commands to carryout cloud GPU training, cloud training logs are displayed in real time, and training results are automatically fed back by multiple platforms; a deep learning development environment is logged in andautomatically installed at a local terminal, dependence on the development environment is automatically detected and installed, the algorithm development process is omitted, and a network structure method is rapidly iterated. Codes are submitted to a cloud GPU for automatic training and evaluation through a convenient script command, a training log is displayed in real time, and a training resultis fed back in time.

Description

technical field [0001] The invention belongs to the technical field of competition data processing, and in particular relates to a full-process artificial intelligence competition system based on a local model and cloud feedback and a data processing method thereof. Background technique [0002] Artificial Intelligence (Artificial Intelligence), the English abbreviation is AI. It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. [0003] Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a manner similar to human intelligence. Research in this field includes robotics, language recognition, image recognition, natural language processing and expert systems, etc. Since the birth of artificial intelligence, the theory and technology have becom...

AI Technical Summary

Problems solved by technology

[0025] However, the inventor further found that the above method is only a general introduction, and there is no specific technical solution. Moreover, the installation of the development environment in the local device and the dependency of the deep learning framework often encounter problems such as version incompatibility, and the learning framework cannot be installed. , the environment preparation work before implementing the algorithm consumes a long time and energy;

[0026] In addition, local model training is especially for large-scale neural network structure training, and the CPU in ordinary local devices cannot meet the requirements of model calculation.

And configuring the GPU hardware device of the local device (PC, etc.) requires a huge cost

[0027] After the local device performs model training and completes, it is necessary to input the verification set data into the model again to obtain the prediction result file, and submit the prediction result file for model quality evaluation. This procedure is cumbersome and increases the iteration time cost of the model.

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