CN112799742A - Machine learning training system and method based on micro-service - Google Patents
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Abstract
The invention provides a machine learning practical training system based on microservice, which comprises a user data management service and a user operation interface, wherein the user data management service is used for responding to operation actions of a user, storing user related information and providing the user operation interface, and the user related information comprises a user name, a mobile phone number, a login password and user operation data; the task management service is connected with the user management service and used for providing a task management interface and maintaining the tasks and task states of the user; the data storage service is connected with the user management service and is used for providing storage and modification operations of the data set; the executive machine management service is connected with the task management service to realize the registration and discovery of the executive machine and allocate computing resources to tasks through a scheduling algorithm; the computing resource comprises a plurality of execution machines, and the execution machines are computers provided with machine learning code operating environments and are used for executing computing tasks; and simultaneously calls the task management service to record the starting execution time and the task state.
Description
Technical Field
The invention relates to the technical improvement field of micro-services, in particular to a machine learning practical training system based on micro-services.
Background
The technical personnel in the past need to install and configure a complete development environment before programming to run the written code, but it is difficult for beginners just contacting programming, especially when needing to configure various development environments, various errors often occur in the process, and the idea of giving up learning is shown. Especially, in the development environment related to machine learning, more software libraries need to be installed, even the hardware platform needs to be concerned, and the installation package adapted to the hardware is installed, otherwise, the normal operation cannot be realized.
The online compiler is really a good choice when it is overwhelmed by environmental issues. Compared with a local environment, the online compiler is very lightweight, the website can be obtained by opening the website at any time, the configuration is not needed, the code is typed in, the result can be obtained at once by RUN, and the online compiler can be switched back and forth among a plurality of compilers. On-line compilers of various programming languages exist at present, such as codingground [ https:// www.tutorialspoint.com/codingground.htm ], techio [ https:// tech.io/snippet ] and texter [ https:// texter.com ], and through analysis of the above three existing on-line compilers, the compilers only provide a basic programming language operating environment, do not have installation of some high-level tool libraries, and cannot meet the operating requirements of machine learning codes.
A series of processes for applying machine learning techniques typically include: converting real world problems to machine learning problems, collecting data, performing feature engineering, selecting or designing model architecture, adjusting model parameters, evaluating model performance, deploying machine learning to online services, and maintaining high availability of services. To complete the series of processes from beginning to end, a business problem expert, a data scientist, an algorithm expert, a development engineer capable of engineering an algorithm, an operation and maintenance engineer capable of ensuring the external stable service of the system are often needed, and in addition, people are also needed to be responsible for solving the problems of large-scale cluster operation and maintenance and the like which are necessary for supporting training, model service and the like. Such a complex process and personnel configuration, for the students just entering the door, especially at school, who are learning, to experience machine learning in a short period of time is simply not possible.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide a micro-service-based machine learning practical training system and method, through the platform, common students can build simple machine learning models, and for more experienced students and teachers, detailed work such as environment building, model deployment, service operation and maintenance, resource application and the like is not needed, the time for deploying the models is reduced from a day level to a minute level, and the learning interest of the students and the working efficiency of the teachers are greatly improved.
To achieve the above and other related objects, the present invention provides a machine learning training system based on microservice, the system comprising:
the user data management service is used for responding to the operation action of the user, storing the relevant information of the user and providing a user operation interface, wherein the relevant information of the user comprises a user name, a mobile phone number, a login password and user operation data;
task management service, which is connected with the user management service and is used for providing a task management interface and maintaining tasks and task states of users, wherein the task states at least comprise: an initial state, a waiting state, an execution state, a stop state and a completion state;
a data storage service, connected to the user management service, for providing storage and modification operations for data sets, the modification operations including: adding, retrieving, updating and deleting operations to enable a user to query the public data set and the autonomously uploaded data set;
the executive machine management service is connected with the task management service, realizes the registration and discovery of the executive machine and distributes computing resources to tasks through a scheduling algorithm;
the computing resource comprises a plurality of execution machines, and the execution machines are computers provided with machine learning code operating environments and used for executing computing tasks; and simultaneously calls the task management service to record the starting execution time and the task state.
In one implementation, the user data management service is configured to: the task management service is called to register the task in the idle state, and a task list is obtained from the task management service and displayed on a user interface; and uploading a custom machine learning training data set to the data storage service, and obtaining a data set list from the data storage service.
In one implementation, the user data management service includes a web application and a web database;
the web application is used for responding to the operation action of the user;
the web database stores user related information, wherein the user related information comprises a user name, a mobile phone number, a login password and user operation data.
In one implementation, the execution engine management service is configured to assign an execution engine to a task in an idle state to change the task to a wait state, and manage the execution engine using the registration and discovery feature of the microservice, and register the execution engine with the execution engine management service after the execution engine is started.
In addition, a machine learning practical training method based on micro service is also disclosed, and the method comprises the following steps:
running task data preset by a user through a user data management service, and adding the task into a task list;
creating a task in an initial state for the task based on the task list through a task management service;
detecting a task having the initial state via the executive management service, allocating computing resources to the task, wherein the computing resources comprise at least one executive, and changing the task to be in a waiting state;
the execution machine detects the task belonging to the execution machine, changes the task into an execution state after executing the task, and stores the running data after the task is executed, wherein the task is in a completion state.
As described above, the machine learning practical training system and method based on microservices provided by the embodiments of the present invention provide a lightweight machine learning algorithm operating environment for relevant students, scientific researchers, and engineers from the perspective of users, and can be used on any device equipped with a web browser; because the computer resource execution machine can be dynamically registered, under certain authorization, a user can install client software and register the computer of the user into the execution machine, thereby achieving the purpose of fully utilizing the computer resource. From the perspective of a system manager, the system is realized through highly decoupled fine-grained micro-services, the micro-services are smaller in granularity and are concentrated on completing a small task, so that each micro-service can be independently deployed and expanded, and the machine learning practical training environment has the advantages of being high in expansibility, high in maintainability, high in elasticity, low in dependency, capable of effectively supporting DevOps, technical heterogeneity and the like.
Drawings
Fig. 1 is a schematic structural diagram of a micro-service based machine learning training system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a specific application of the micro-service based machine learning training system according to the embodiment of the present invention.
Fig. 3 is a schematic diagram of another specific application of the microservice-based machine learning practical training system according to the embodiment of 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.
Please refer to fig. 1-3. It should be noted that the drawings provided in the present embodiment are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Fig. 1-2 show a machine learning training system based on microservice according to an embodiment of the present invention, including: the user data management service is used for responding to the operation action of the user, storing the relevant information of the user and providing a user operation interface, wherein the relevant information of the user comprises a user name, a mobile phone number, a login password and user operation data; task management service, which is connected with the user management service and is used for providing a task management interface and maintaining tasks and task states of users, wherein the task states at least comprise: an initial state, a waiting state, an execution state, a stop state and a completion state; a data storage service, connected to the user management service, for providing storage and modification operations for data sets, the modification operations including: adding, retrieving, updating and deleting operations to enable a user to query the public data set and the autonomously uploaded data set; the executive machine management service is connected with the task management service, realizes the registration and discovery of the executive machine and distributes computing resources to tasks through a scheduling algorithm; the computing resource comprises a plurality of execution machines, and the execution machines are computers provided with machine learning code operating environments and used for executing computing tasks; and simultaneously calls the task management service to record the starting execution time and the task state.
It should be noted that the machine learning training environment of the present invention employs a micro-service architecture, the micro-service has a clear context boundary to enhance the modularization of the application, each service component can be synchronously developed without interference or conflict, the development speed is much faster than that of the traditional application architecture, and further agile development iteration is realized, and each service module can be quickly built only by determining the role of each component in the architecture. The working principle of each functional service in the system of the invention is as follows:
user data management service: user information, user operational data (code, data sets, models, etc.) are maintained. For example, comprising a web application and a web database, through which information received by the web application is stored.
The user data management service is also a UI user operation interface, the main interface of which is shown in fig. 3. Its main functions are to respond to the user's operation actions and to store user-related information. The user related information comprises a user name, a mobile phone number, a login password, user operation data and the like.
Two services which interact with the user data management service are respectively a task management service and a data storage service. The service calls a task management service to register a task in an idle state, at the moment, fields contained in the task are shown in a third area of an initial state node in the graph 1, and task data are stored in the task management service; the service can acquire a task list from the task management service and display the task list to a user interface; the service can upload a user-defined machine learning training data set to the data storage service; the service may obtain a list of data sets from a data storage service.
Task management service: the tasks and task states (initial state, wait state, execute state, stop state, and complete state) of all users are maintained.
The task management service is a more important service in the system and mainly provides a task management interface. Providing the operations of adding, modifying, deleting and inquiring tasks.
First, as shown in fig. 1, the embodiment of the present invention provides a corresponding relationship between various states and task attributes, and correspondingly, descriptions of the task attributes and their corresponding relationships are given in table 1 and table 2, respectively. The method comprises the steps of starting a task at start, setting the task as an initial state idle, setting the task as a wait waiting state after the task is distributed to a server, starting execution to enter an execution state Run after the waiting condition is finished, setting the task as a finish state after the execution is finished, setting the task as a stop state if the execution is stopped, restarting the task after the execution is started, and setting the wait waiting state. The task can be terminated by deleting the task when the task is in wait state, stop state, idle initial state and finish completion state.
TABLE 1
| Task attributes | Description of the invention |
| task_num | Task numbering |
| data_set | Data set |
| code_content | Code content |
| code_tag | Code label (operation environment) |
| create_time | Task creation time |
| server_info | Information of execution machine |
| start_time | Start run time |
| stop_time | Stopping running time |
| finish_time | End runtime |
| task_output | Task execution reporting |
TABLE 2
| Status name | Description of the invention |
| IDLE | Initial state |
| WAIT | Wait state |
| RUN | Execution state |
| Stop | Rest state |
| FINISH | Completion status |
As shown in fig. 2, the task management system includes task management and a task database, specifically, the task management may be a software program for performing task management, and is used to implement the task management according to the present invention, and specifically, a person skilled in the art may implement the present invention through a software code without detailed description.
Data storage service: storage of data sets and CRUD operations (add (Create), Retrieve (Retrieve), Update (Update) and Delete (Delete)) are provided.
The data storage service stores training data sets, and users can query public data sets and autonomously uploaded data sets.
An execution machine: a computer having a machine learning code execution environment installed therein actively registers and merges into the system with the execution machine management service by starting the computing service.
The execution machine refers to a series of dynamic services, which respectively search the service belonging to the wait state from the task management service, acquire a required data set from the data storage service according to the searched task data, and call the task management service to record the start execution time (start _ time), wherein the task is in a run state; after the task execution is finished, the task management service is called to record execution result data (task _ output and finish _ time), and the task is in a finish state at the moment. If the run state is present, the task is stopped, and the stop time (stop _ time) is recorded, wherein the task is in the stop state.
Executing machine management service: and realizing the registration and discovery of the execution machine, and allocating the execution machine computing resources to the tasks through a scheduling algorithm.
As shown in fig. 2, the execution machine management service includes execution machine management and an execution machine database, specifically, the execution machine management may be a software program for executing the execution machine management, and is used to implement the execution machine management according to the present invention, specifically, a person skilled in the art may implement the present invention by using a software code without detailed description.
The executive management service is a scheduling service, and the main function of the executive management service is to allocate an executive to a task in an idle state so that the task can be identified by the executive and can be subsequently processed. When the execution machines are required to be distributed, some execution machines need to be managed, the execution machine management service utilizes the registration of the microservice and the characteristic discovery management execution machine, and the execution machine is registered to the service after being started; before the executive machine management service distributes the executive machines to the tasks in idle states, health check is carried out, and the executive machines are found.
One microservice architecture system, in addition to a wide variety of services, another important element is communication between services. The communication relationship between the services of the system of the present invention is shown in fig. 2. The specific information interaction mode, communication protocol, implementation framework, and message format are explained below:
1. RESTful protocol
REST is essentially an architectural style in which presentation state transitions are a set of architectural constraints and principles. While software system architectures that satisfy this architectural style are considered RESTful. RESTful architecture emphasizes: 1) locating the resource using the URI; 2) performing resource operation through the expression, wherein the resource can be XML, JSON, binary files and the like; 3) the client makes the server generate the presentation layer state conversion by using 4 HTTP verbs (GET, POST, PUT, DELETE). The RESTful API refers to an API that makes calling resources very convenient and intuitive, while also reducing the complexity of the service.
Input and output interfaces related to the user data management service are RESTful, which is a communication protocol commonly used for Web development, and other protocols are not introduced, so that the independence of the service is maintained. As in fig. 2, communication using the RESTful interface is: registering tasks, task lists, uploading data and data lists.
2. RPC protocol
RPC (remote Procedure Call) is the "remote Procedure Call". An RPC is a service that allows a program to call a class method or function in another address space, usually on another machine. The method is a technology which is erected on a computer network and hides an underlying network, can call a remote program like calling a local service, and improves the throughput capacity under the condition of low coding cost.
The micro-service RPC framework has various types, the gRPC is used by Google, the gRPC is a high-performance framework for realizing RPC service for Google development and source opening, and is based on an http2.0 protocol, and the working efficiency of the framework is higher than that of a RESTful framework based on an http1.1 protocol. The method call, call parameters, response parameters and the like are transmitted between two servers, the parameters need to be serialized, the gRPC adopts the syntax of a protocol buffer (check proto), the method, the parameters and the response format to be called can be defined through the proto syntax, the remote method call can be conveniently completed, and the parameter extension and the parameter update are very facilitated.
The invention uses RPC mode to communicate with other back-end service except user data management service. As shown in fig. 2, the gRPC framework is used for communication: allocating an executive machine, discovering an idle task, executing a task, discovering a wait task, registering a service, discovering the service, naming a data set and a data file; fig. 3 is a design drawing of a user operation panel in a machine learning practical training environment according to an embodiment of the present invention.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (5)
1. A machine learning training system based on microservice, the system comprising:
the user data management service is used for responding to the operation action of the user, storing the relevant information of the user and providing a user operation interface, wherein the relevant information of the user comprises a user name, a mobile phone number, a login password and user operation data;
task management service, which is connected with the user management service and is used for providing a task management interface and maintaining tasks and task states of users, wherein the task states at least comprise: an initial state, a waiting state, an execution state, a stop state and a completion state;
a data storage service, connected to the user management service, for providing storage and modification operations for data sets, the modification operations including: adding, retrieving, updating and deleting operations to enable a user to query the public data set and the autonomously uploaded data set;
the executive machine management service is connected with the task management service, realizes the registration and discovery of the executive machine and distributes computing resources to tasks through a scheduling algorithm;
the computing resource comprises a plurality of execution machines, and the execution machines are computers provided with machine learning code operating environments and used for executing computing tasks; and simultaneously calls the task management service to record the starting execution time and the task state.
2. The microservice-based machine learning training system of claim 1, wherein the user data management service is configured to: the task management service is called to register the task in the idle state, and a task list is obtained from the task management service and displayed on a user interface; and uploading a custom machine learning training data set to the data storage service, and obtaining a data set list from the data storage service.
3. The microservice-based machine learning training system of claim 1, wherein the user data management service comprises a web application and a web database;
the web application is used for responding to the operation action of the user;
the web database stores user related information, wherein the user related information comprises a user name, a mobile phone number, a login password and user operation data.
4. The microservice-based machine learning training system of claim 1, wherein the executive management service is configured to assign an executive to a task in idle state, change the task to wait state, and manage the executive using the registration and discovery feature of the microservice, the executive registering with the executive management service after starting.
5. A machine learning practical training method based on micro service is characterized by comprising the following steps:
running task data preset by a user through a user data management service, and adding the task into a task list;
creating a task in an initial state for the task based on the task list through a task management service;
detecting a task having the initial state via the executive management service, allocating computing resources to the task, wherein the computing resources comprise at least one executive, and changing the task to be in a waiting state;
the execution machine detects the task belonging to the execution machine, changes the task into an execution state after executing the task, and stores the running data after the task is executed, wherein the task is in a completion state.
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