CN115643249A - Construction method of AI teaching practical training programming platform based on Web page - Google Patents
Construction method of AI teaching practical training programming platform based on Web page Download PDFInfo
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Abstract
The invention discloses a construction method of an AI teaching practical training programming platform based on a Web page. The method of the invention comprises the following steps: (1) Constructing a bottom cloud native environment, including a Kubernets cluster, a Ceph storage layer, a Harbor mirror image management and a Nacos service discovery; (2) The back end adopts the micro-service technology, is deployed on a Kubernets cluster in a Pod form, is registered on Nacos, and performs service discovery and access through a unified gateway entrance; (3) The front end also adopts the micro-service technology and realizes unified access through the Nginx agent; (4) And establishing a database for storing some platform-related information such as users and resources of the platform. The invention enables users to conveniently, quickly and efficiently learn and apply artificial intelligence technology. The system solves a series of problems that the personal development and construction environment of the user is not uniform, multi-user teaching is not convenient, the user management efficiency is low and the like.
Description
Technical Field
The invention relates to the fields of artificial intelligence, recommendation algorithm and automatic machine learning, in particular to a construction method of an AI teaching practical training programming platform based on a Web page.
Background
Along with the arrival of the artificial intelligence era, more and more universities and scientific research institutes develop AI course teaching, but AI learning environment versions are complicated and depend on a lot, and the Tensorflow has many versions, let alone the version management of other AI software such as Pyorch, to new people installation and unfriendly, and can cause many people's many environmental situation, be unfavorable for the unified teaching management of teacher. A uniform, efficient, stable AI learning environment must be employed.
In order to solve the problems, a plurality of AI orchestration platforms, such as KubeFlow, MLFlow, etc., are emerging in the market, but the platforms also have some problems, some of which are heavy, and the installation and deployment of individuals are difficult; some of the teachers are lack of efficient and useful unified management functions, and cannot manage conveniently and effectively; meanwhile, the personal environment resource allocation of the user does not necessarily meet the minimum requirement of the platform and can not experience the whole flow of fluent learning.
Disclosure of Invention
The invention aims to: aiming at the problems and the defects in the prior art, the invention aims to provide a construction method of an AI teaching practical training programming platform based on a Web page, which integrates the current mainstream AI technology and cloud native frameworks including Kubernets, ceph, habor and the like, is good from learning, storage, query and calculation, covers all parts in an AI teaching system, and simultaneously abandons the defects of the native frameworks: the method has the advantages that the method has the defects of inflexibility on interaction with a user and difficulty in starting, the use methods of the technologies are reconstructed on the webpage, and the use method which is easy to learn and use is provided, so that the user can conveniently, quickly and efficiently learn and apply the artificial intelligence technology. The system solves a series of problems that the personal development and construction environment of the user is not uniform, multi-user teaching is not convenient, the user management efficiency is low and the like.
The technical scheme is as follows: in order to achieve the purpose, the technical scheme adopted by the invention provides a method for constructing an AI teaching practical training programming platform based on a Web page, which comprises the following steps:
(1) Constructing a bottom cloud native environment, including a Kubernets cluster, a Ceph storage layer, a Harbor mirror image management and a Nacos service discovery;
(2) The back end adopts the micro-service technology, is deployed on a Kubernets cluster in a Pod form, is registered on Nacos, and performs service discovery and access through a uniform gateway entrance;
(3) The front end also adopts the micro-service technology and realizes unified access through the Nginx agent;
(4) Establishing a database for storing some platform-related information such as users and resources of the platform;
remote communication can be carried out between the back-end service in the step (2) and the front-end service in the step (3), and the back-end service and the front-end service are deployed in independent environments; in the step (2), the back-end service receives each request of the front-end service and performs authority verification to prevent malicious attack.
Further, in the step (1), a cloud native foundation environment kubernets cluster is deployed first; deploying a Ceph storage layer based on the Kubernets cluster, wherein after the Ceph is deployed, default storage of the Kubernets cluster bottom layer is set to be Ceph, and the Ceph is used for providing bottom-layer distributed storage service for the whole Kubernets cluster and various services thereon; then deploying a mirror image management warehouse Harbor based on a Kubernetes cluster, wherein the Harbor provides a management function for a user to use a default practical training environment mirror image and a user-defined practical training environment mirror image; and finally, deploying the Nacos service based on the Kubernets cluster, and using the Nacos service for service discovery of the back-end micro-service.
Further, in the step (2), corresponding functional micro-service modules are developed according to the interface functions of the front-end service, and each micro-service module is responsible for processing different types of corresponding interface functions; then aiming at configuration parameters needing dynamic change, such as IP addresses, port numbers and host lists, uniformly extracting configuration files or setting the configuration files into environment variables of a Kubernets cluster, wherein the configuration files and the environment variables of the Kubernets cluster comprise partial service internal VIPs, database configuration and configuration parameters of some matched services, each functional micro-service module has a special configuration parameter, and finally, the parameters corresponding to all micro-services are matched into a starting file or set into the environment variables of the Kubernets cluster, so that the corresponding micro-service modules are correctly started; when front-end interface function requests are processed, each request is responded by a special method and is controlled by a spring cloud implementation method, then specific requirements are sent to a corresponding bottom-layer cluster framework or micro-service to be implemented, and then operation results are fed back to an interface for display; and all data generated by the user in the using process, including the operation log, is stored in the database.
Furthermore, in the step (3), the front-end micro-service of the platform is compiled and packaged to the specified directory, and meanwhile, the corresponding configuration is written into the configuration file, including the communication IP with the back-end micro-service and the port configuration information, and the unified entry access is realized through the Nginx proxy, and the configuration file is read when the front-end micro-service is started, so that the communication with the back-end micro-service is realized; each interface main function is an independent micro application module, the whole platform is composed of a plurality of different micro application modules, and the administrator functions comprise the following micro application modules: the system comprises nine modules, namely a home page, big data service, my class, educational administration management, a data center, experimental projects, user service, resource management and an operation log, wherein each module has a corresponding function; the teacher function comprises the following micro application modules: the system comprises six modules, namely a homepage, educational administration management, a data center, an experimental project, user service and resource management, wherein each module has a corresponding function; the student functions include the following micro application modules: the system comprises five modules including a home page, a my classroom, a data center, an experimental project and a data set configuration, and each module has a corresponding function.
Further, in the step (4), each micro-service corresponds to a database, different databases are isolated from each other, the database service is installed and deployed, then the user authority of the micro-service is opened, if a specified user uses a correct password, the user can log in and use the database service at any address, and the back end stores platform-related data such as user data, experimental data, user group data, document data and the like in the database.
Further, remote communication is performed between the back-end service in the step (2) and the front-end service in the step (3), the two services are deployed in independent environments, specifically, after the front end and the back end are separated, the front-end service and the back-end service can be independently operated on different nodes, decoupling is completed between the two services, or a high-availability system is constructed through load balancing.
Further, in the step (2), the authority verification is performed when the back-end service receives each request of the front-end service, so as to prevent malicious attacks.
Has the beneficial effects that: the invention can deploy a Web-based AI teaching practical training programming platform based on some universal servers, and solves some problems frequently encountered by users when establishing the AI platform, such as: the model selection is difficult, the installation configuration threshold is high, the unified user management is inconvenient, the local development environment is difficult to build, the AI technology is easy to learn and use by novices or experienced workers carry out development or scientific research operation, and the method plays a very positive role in the technical education of the AI industry.
Drawings
FIG. 1 is a schematic diagram of the overall process under the role of administrator according to the present invention;
FIG. 2 is a schematic flow chart of the present invention in a teacher role;
FIG. 3 is a schematic view of the overall process of the present invention under the role of student;
FIG. 4 is a schematic diagram of a front end service to administrator role function module;
FIG. 5 is a schematic diagram of a front-end services to teacher role function module;
FIG. 6 is a schematic diagram of a front-end service to student role function module;
fig. 7 is a schematic diagram of a backend service.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
The invention provides a method for constructing an AI teaching practical training programming platform, which is characterized in that a micro-service on a cloud primary cluster built based on a universal server is deployed, so that a user can perform interactive operation with the micro-service through a webpage, the user can perform AI course learning, AI project practical operation and the like on the webpage, all user code execution operations are transmitted to the cloud primary cluster through the micro-service to be executed, results are fed back to the webpage for display, all code development, data set operation, mirror image operation and the like are performed, the user can directly perform management operation on a unified page without environment switching, page multi-opening and the like, and accordingly, one-stop AI learning practical training programming environment construction is achieved.
As shown in fig. 1 to 3, the complete process of the present invention includes four parts: the method comprises the following steps of customized construction and configuration of a bottom cloud native cluster, deployment of back-end micro-services based on cloud native, construction of a database and construction of front-end services. The specific implementation flow is described as follows:
the customized construction and configuration of the bottom cloud native cluster correspond to the technical scheme step (1), and the specific implementation mode is as follows: firstly, deploying a cloud native foundation environment Kubernetes cluster (hereinafter referred to as K8S); deploying a Ceph storage layer based on K8S, setting the default storage of the K8S bottom layer as Ceph after the Ceph is deployed, wherein the Ceph is used for providing bottom-layer distributed storage service for the whole K8S and various services thereon; then deploying a mirror image management warehouse Harbor based on K8S, wherein the Harbor provides a management function for a user to use a default practical training environment mirror image and a custom practical training environment mirror image; and finally, deploying the Nacos service based on the K8S for service discovery of the back-end micro-service. Thus, the construction of the whole cloud native environment is completed.
The construction of the back-end micro service corresponds to the technical scheme step (2), and the specific implementation mode is as follows: firstly, developing corresponding functional micro-service modules according to interface functions of front-end services, wherein each micro-service module is responsible for processing different types of corresponding interface functions, for example, constructing an AI practical training environment is one micro-service module, and AI course learning management is the other micro-service module; then aiming at configuration parameters needing to be dynamically changed, such as an IP address, a port number and a host list, a configuration file is uniformly extracted or is set into an environment variable of K8S, the configuration file and the environment variable of K8S comprise partial VIP (very important person), database configuration and configuration parameters of some matched services inside the service, each functional micro-service module has a special configuration parameter, and finally, parameters corresponding to all micro-services are matched in a starting file or set into the environment variable of K8S, so that the corresponding micro-service module is correctly started; when front-end interface function requests are processed, each request is responded by a special method and is controlled by a spring cloud implementation method, then specific requirements are sent to a corresponding bottom-layer cluster framework or micro-service to be implemented, and then operation results are fed back to an interface for display; and all data generated by the user in the using process, including the operation log, is stored in the database. All back-end micro services are deployed and operated based on a cloud native environment, so that deployment and use in a new server cluster are facilitated, the environment comprises all software and AI (Artificial intelligence) operation environments required by the back-end micro services, and all teaching and practical training performed on an interface are performed based on the micro services.
The construction of the front-end service corresponds to the technical scheme step (3), and the specific implementation mode is as follows: the method comprises the steps of compiling and packaging front-end micro services of a platform to a specified directory, writing corresponding configurations of the front-end micro services into configuration files, wherein the configuration files comprise configuration information such as communication IP (Internet protocol) and ports of the back-end micro services, realizing unified entry access through an Nginx agent, and reading the configuration files when the front-end micro services are started so as to realize communication with the back-end micro services. Each interface main function is an independent micro application module, the whole platform is composed of a plurality of different micro application modules, and the administrator functions mainly comprise the following micro application modules: the system comprises nine modules, namely a home page, a big data service, a my classroom, educational administration, a data center, an experimental project, user service, resource management and an operation log, wherein each module has a corresponding function. The teacher function mainly comprises the following micro application modules: the system comprises six modules including a homepage, educational administration management, a data center, an experimental project, user service and resource management, wherein each module has a corresponding function. The student functions mainly comprise the following micro application modules: the system comprises five modules including a home page, a my classroom, a data center, an experimental project and a data set configuration, and each module has a corresponding function. The request initiated by the front-end microservice has the following three processing modes:
partial independent verification can directly return results after front-end processing;
after the module function request related to the authority verification is processed at the front end, the request is sent to the back end for authentication and a result is returned;
operation requests related to the cloud native cluster, such as establishment of a training environment and the like, are sent to the back-end micro-service for preliminary processing, are uniformly dispatched to the cluster by the back-end micro-service for execution, and corresponding information is fed back to the front-end interface.
The database is built correspondingly to the technical scheme step (4), and the specific implementation mode is as follows: the database service is installed and deployed, then the user authority of the database service is opened, and if a specified user uses a correct password, the user can log in to use the database service at any address. The back end stores the user data, experimental data, user group data, document data and other platform related data in the database.
Claims (7)
1. A construction method of an AI teaching practical training programming platform based on a Web page is characterized by comprising the following steps:
(1) Constructing a bottom cloud native environment, including a Kubernets cluster, a Ceph storage layer, a Harbor mirror image management and a Nacos service discovery;
(2) The back end adopts the micro-service technology, is deployed on a Kubernets cluster in a Pod form, is registered on Nacos, and performs service discovery and access through a unified gateway entrance;
(3) The front end also adopts the micro-service technology and realizes unified access through the Nginx agent;
(4) Establishing a database for storing some platform-related information such as users and resources of the platform;
remote communication can be carried out between the back-end service in the step (2) and the front-end service in the step (3), and the back-end service and the front-end service are deployed in independent environments; and (3) in the step (2), the back-end service receives each request of the front-end service and performs authority verification to prevent malicious attack.
2. The method for constructing the AI teaching practical training programming platform based on the Web page as recited in claim 1, wherein in the step (1), a cloud native foundation environment Kubernets cluster is deployed first; the method comprises the steps that a Ceph storage layer is deployed on the basis of a Kubernetes cluster, after Ceph is deployed, default storage of a Kubernetes cluster bottom layer is set to be Ceph, and the Ceph is used for providing bottom-layer distributed storage service for the whole Kubernetes cluster and various services on the Kubernetes cluster; then deploying a mirror image management warehouse Harbor based on a Kubernetes cluster, wherein the Harbor provides a management function for a user to use a default practical training environment mirror image and a user-defined practical training environment mirror image; and finally, deploying the Nacos service based on the Kubernets cluster, and using the Nacos service for service discovery of the back-end micro-service.
3. The method for constructing the AI teaching practical training programming platform based on the Web page as recited in claim 1, wherein in said step (2), corresponding functional micro-service modules are first developed according to the interface functions of the front-end service, each micro-service module being responsible for processing the interface functions corresponding to different types; then aiming at configuration parameters needing dynamic change, such as IP addresses, port numbers and host lists, uniformly extracting configuration files or setting the configuration files into environment variables of a Kubernets cluster, wherein the configuration files and the environment variables of the Kubernets cluster comprise partial service internal VIPs, database configuration and configuration parameters of some matched services, each functional micro-service module has a special configuration parameter, and finally, the parameters corresponding to all micro-services are matched into a starting file or set into the environment variables of the Kubernets cluster, so that the corresponding micro-service modules are correctly started; when front-end interface function requests are processed, each request is responded by a special method, is controlled by a method realized by spring cloud, then sends specific requirements to a corresponding bottom-layer cluster architecture or micro-service for realization, and then feeds back operation results to an interface for display; all data generated by the user during the use process, including the operation log, is stored in the database.
4. The method for constructing the AI teaching practical training programming platform based on the Web page according to claim 1, wherein in step (3), the front-end micro-services of the platform are compiled and packaged to the specified directory, and the corresponding configuration is written into the configuration file, including the communication IP with the back-end micro-services and the port configuration information, and the unified entry access is realized through the Nginx agent, and the configuration file is read when the front-end micro-services are started, so as to realize the communication with the back-end micro-services; each interface main function is an independent micro application module, the whole platform is composed of a plurality of different micro application modules, and the administrator functions comprise the following micro application modules: the system comprises nine modules, namely a home page, big data service, my classroom, educational administration, a data center, experimental projects, user service, resource management and an operation log, wherein each module has a corresponding function; the teacher function comprises the following micro application modules: the system comprises six modules, namely a homepage, educational administration management, a data center, an experimental project, user service and resource management, wherein each module has a corresponding function; the student functions include the following micro application modules: the system comprises five modules of a home page, a my classroom, a data center, an experimental project and data set configuration, and each module has a corresponding function.
5. The method for constructing the AI teaching practical training programming platform based on the Web page as claimed in claim 1, wherein in the step (4), each micro-service corresponds to a database, different databases are isolated from each other, the database service is installed and deployed, then the user right of the micro-service is opened, if a specified user uses a correct password, the user can log in and use the database service at any address, and the back end stores platform-related data such as user data, experimental data, user group data, document data and the like in the database.
6. The method for constructing the AI teaching practical training programming platform based on the Web page as recited in claim 1, wherein the backend service of step (2) and the frontend service of step (3) are remotely communicated with each other, and the two services are deployed in an independent environment, specifically, after the frontend and the backend are separated, the frontend and the backend can independently operate on different nodes, and the decoupling between the frontend and the backend is completed, or a highly available system is constructed through load balancing.
7. The method for constructing the AI teaching practical training programming platform based on the Web page as claimed in claim 1, wherein the back-end service performs authority verification to prevent malicious attacks when receiving each request of the front-end service in step (2), and the specific method is that for each user of the platform, a unique token is assigned, the token is an identity identifier, and each operation performed on the platform verifies the token to ensure that the operation is not a malicious attack.
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| CN117524445A (en) * | 2023-10-19 | 2024-02-06 | 广州中康数字科技有限公司 | Medical field artificial intelligence engineering platform based on micro-service and containerization technology |
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| CN117524445A (en) * | 2023-10-19 | 2024-02-06 | 广州中康数字科技有限公司 | Medical field artificial intelligence engineering platform based on micro-service and containerization technology |
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