CN113076998A - Distributed classification method based on kubernets deep neural network model - Google Patents

Abstract

The invention relates to a distributed classification method based on a kubernets deep neural network model, and belongs to the field of deep learning and cloud computing. S1: acquiring a data set, labeling the data set, and carrying out standardized processing, wherein the data is divided into a training set and a test set; inputting the training set after the standardization treatment into a plurality of different deep neural network models for training; inputting a test set to obtain the accuracy of the corresponding model, and distributing weight according to the high accuracy; s2: deploying a model; and (3) adopting a kubernets container arrangement system to reasonably deploy a classification model. The invention uses the container technology, solves the problem of larger inference delay of the deep neural network model, and simultaneously utilizes the idea of integrated learning to ensure that the inference capability of the model is stronger.

Description

Distributed classification method based on kubernets deep neural network model

Technical Field

The invention belongs to the field of deep learning and cloud computing, and designs a distributed classification method based on a kubernetes deep neural network model.

Background

The deep learning is a branch of machine learning, is an algorithm which takes an artificial neural network as a framework and performs characterization learning on data, and basically uses a deep learning model in a classification task along with the development of the deep learning.

Although the deep neural network model is superior in classification task, with the development of the deep neural network model, the parameter quantity of the model is continuously increased, the technical resource requirement is higher and higher, and the actual deployment and application are difficult.

When the deep neural network model classifies the multi-attribute data set, attribute combination is rarely concerned, the characteristic attributes are directly input into the deep neural network training model, and then the trained model is used for sample classification. For example, in some deep neural networks which are popular at present, due to large parameter quantity and high difficulty in obtaining models, the effect of actually deploying the models is not ideal.

Disclosure of Invention

In view of this, the invention aims to provide a distributed classification method based on a kubernets deep neural network model, which solves the problem of large inference delay of the deep neural network model, and meanwhile, the inference capability of the model is stronger by using the idea of ensemble learning for reference.

In order to achieve the purpose, the invention provides the following technical scheme:

a distributed classification method based on a kubernets deep neural network model comprises the following steps:

s1: training a model;

s11: acquiring a data set, which can be any data set to be classified;

s12: labeling the data set;

s13: carrying out standardization processing on a data set, and then dividing the data set into a training set and a testing set;

s14: respectively inputting the training set after the standardization treatment into n different deep neural network models for training to obtain a model f₁,f₂,f₃,...,f_n(ii) a I.e. the same data set is judged comprehensively by using a plurality of deep learning models.

S15: inputting a test set to obtain the accuracy of the corresponding model, and distributing the weight W according to the high accuracy₁,W₂,W₃,...,W_n；

S2: deploying a model;

before deployment, a kubernets cluster is established, a master manages nodes of a plurality of workers, a plurality of pods run tensoflow serving, and the deployment of the pods can be deployed according to specific resource conditions; all resources establish connection through service; the trained n deep neural network models are deployed in tensoflow-serving; and the external part accesses the tenserflow-serving through the api to obtain an inference result.

S3: and testing real-time data to obtain a final result, and specifically comprising the following steps of:

s31: acquiring real-time data;

s32: carrying out standardization processing on the acquired real-time data;

s33: obtaining different reasoning results from the standardized data by accessing the tensoflow-serving api;

s34: and (3) adopting a Weighted voting algorithm (Weighted voting algorithm), and carrying out Weighted summation on the results obtained by the real-time data inference by different models to obtain the final result.

Further, the normalization process is:

i.e. the characteristic value x of the data_iThe mean value mu is subtracted and then divided by the standard deviation sigma, so that the data are subjected to standard normal distribution, and the calculation amount is reduced.

Further, the deep neural network model may include: resnet, Incepton, VGG, Densenet, and the like. The hyper-parameter selection of the deep learning model can be diversified.

Further, in step S2, kubernets are used as the container arrangement system to monitor, expand and allocate resources for the tensoflow serving pod.

Further, in step S34, according to the method

And calculating to obtain a label value corresponding to the maximum value, and obtaining a label value corresponding to the maximum value.

The invention has the beneficial effects that: the method uses a weight voting mode to comprehensively judge the inferred categories, is similar to ensemble learning, adopts a distributed thought to reduce delay due to the fact that a deep learning algorithm is higher than a traditional model in inference delay, and reasonably deploys a classification model by using a kubernets container arrangement system to reduce inference delay. And finally judging a result, distributing Weight according to the performance of the model in the test set, and carrying out weighted summation on the results obtained by real-time data inference by different models to obtain a final result.

The invention uses the container technology, solves the problem of larger inference delay of the deep neural network model, and simultaneously, makes the inference capability of the model stronger by using the idea of integrated learning.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.

Drawings

For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:

FIG. 1 is a flow chart of a distributed classification method based on a kubernets deep neural network model according to the present invention.

Detailed Description

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.

Referring to fig. 1, the invention preferably selects a distributed classification method based on a kubernets deep neural network model, and designs a distributed classification system by using the idea of ensemble learning and combining with a container technology to improve the reasoning ability of the deep learning model.

Fig. 1 is a schematic flow chart of a distributed classification method according to an embodiment of the present invention, and as shown in fig. 1, the method specifically includes the following steps:

1) and (6) data acquisition.

The present embodiment uses arbitrary data sets, mainly data sets of classification tasks.

2) And (6) data annotation.

3) And (4) preprocessing data.

In the model training stage, the original data are subjected to standardization processing, so that the influence of different dimensions on the data can be eliminated.

And sending the processed image data into a yolov5 model for training, loading pre-training parameters in order to reduce the training time of the model, and training the trained hyper-parameters by adopting yolov5 default configuration parameters.

4) And (4) splitting data.

The data is divided into a training set and a testing set. The training set is used for training the models to obtain different models. The test set is used for obtaining the accuracy of different models and distributing weights to the different models.

5) And (6) deploying the model.

Acquiring different models, deploying the models in different tenserflow serving pods, and connecting the models through services of kubernets.

6) Real-time data.

And after the real-time data is preprocessed, accessing the service of the kubernets through the API to obtain a result.

7) And (6) voting.

The final result is calculated by Weighted votating algorithm.

As other implementation modes, a pod can be designed for training the model on line and then distributing the model so as to obtain the model with better generalization capability.

Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (7)

1. A distributed classification method based on a kubernets deep neural network model is characterized by comprising the following steps:

s1: training a model;

s11: acquiring a data set;

s12: labeling the data set;

s13: carrying out standardization processing on a data set, and then dividing the data set into a training set and a testing set;

s14: respectively inputting the training set after the standardization treatment into n different deep neural network models for training to obtain a model f₁,f₂,f₃,...,f_n；

S15: inputting test set to obtain accuracy of corresponding model, and assigning weight W₁,W₂,W₃,...,W_n；

S2: deploying a model;

before deployment, establishing a kubernets cluster, managing nodes of a plurality of workers by a master, operating tensoflow serving by a plurality of pods, and deploying the pods according to specific resource conditions; all resources establish connection through service; the n trained deep neural network models are deployed in the tensoflow serving.

2. The distributed classification method of claim 1, further comprising: and testing real-time data to obtain a final result, and specifically comprising the following steps of:

s31: acquiring real-time data;

s32: carrying out standardization processing on the acquired real-time data;

s33: obtaining different reasoning results from the standardized data by accessing the tensoflow serving api;

s34: and (3) adopting a weighted voting algorithm, and carrying out weighted summation on the results obtained by real-time data inference by different models to obtain a final result.

3. The distributed classification method according to claim 1 or 2, characterized in that the normalization process is:

i.e. the characteristic value x of the data_iThe mean μ is subtracted and divided by the standard deviation σ to fit the data to a standard normal distribution.

4. The distributed classification method according to claim 1 or 2, wherein in step S15, the weight W is assigned according to the accuracy₁,W₂,W₃,...,W_n。

5. The distributed classification method according to claim 1 or 2, characterized in that the deep neural network model comprises: resnet, Incepton, VGG or Densenet network models.

6. The distributed classification method according to claim 1 or 2, wherein in step S2, tensorflow serving pod is supervised, expanded and resources are allocated using kubernets as a container arrangement system.

7. The distributed classification method according to claim 2, wherein in step S34, the classification is based on

And calculating to obtain a label value corresponding to the maximum value, and obtaining a label value corresponding to the maximum value.

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