CN114064218A - Mirror image generation method, system, medium and application for machine learning component - Google Patents
Mirror image generation method, system, medium and application for machine learning component Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/362—Software debugging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3664—Environments for testing or debugging software
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/60—Software deployment
- G06F8/61—Installation
- G06F8/63—Image based installation; Cloning; Build to order
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/70—Software maintenance or management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45562—Creating, deleting, cloning virtual machine instances
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45575—Starting, stopping, suspending or resuming virtual machine instances
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45591—Monitoring or debugging support
Abstract
The invention belongs to the technical field of mirror image generation, and discloses a mirror image generation method, a mirror image generation system, a mirror image generation medium and application for a machine learning component, wherein the mirror image generation method comprises the following steps: installing a docker tool; generating a new jupyter container; modifying the code of the jupyter controller to ensure that the newly generated jupyter container has a privileged mode; and starting the docker service in the newly-generated jupyter container, and using a docker tool to perform mirroring. According to the mirror image generation method provided by the invention, the docker tool is used in the jupiter environment of the kubeflow container platform, and all the jupiter environments created by the platform can directly use the docker tool to carry out mirror image printing, docker container deployment and other related operations, so that the one-stop operation requirements of debugging, constructing and deploying of algorithm personnel in the jupiter environment are met.
Description
Technical Field
The invention belongs to the technical field of mirror image generation, particularly relates to a mirror image generation method, a mirror image generation system, a mirror image generation medium and application for a machine learning component, and particularly relates to a method for constructing a mirror image by using a docker service in a machine learning jupitter component.
Background
Currently, in the existing mode of the jupyter environment, there are the following two ways for current algorithm personnel to model jupyter:
(1) the local client or server initiates a jupyter service using python and then accesses it through the local browser.
(2) Private cloud and public cloud deployments: the jupyterab service provided by platforms such as hundredth BML and Huacheng ModelArts is deployed on a public cloud through a container or deployed in a notebookserver in a kubeflow private cloud, and a user accesses the service through a local browser.
In order to solve the above technical problems, a CN201110073978.7 programming platform system using description language in the prior art defines system requirements as corresponding function keys, and defines corresponding program blocks, where a program block includes two parts, one part is a program syntax block, and the other part is a program variable block, each program variable block corresponds to a computing mode system, and when in actual application, the program variable block performs operation according to the characteristics described by the user selecting the requirements, so as to obtain a high-level computer program system of corresponding variables.
The second CN201910738817.1 in the prior art discloses a natural language programming method and device based on a strong static scripting language, the method includes: the method comprises the steps that a front-end compiler acquires a first script and meta-information from a database, compiles the first script into a loaded editor renderer and verifies the content in the editor renderer according to the meta-information, wherein the meta-information at least comprises type information; generating a second script through the editor renderer after editing and checking, and storing the second script into a database; and the back-end interpreter acquires the second script and the meta-information from the database, compiles the second script into a runtime object, and verifies the runtime object according to the meta-information.
The prior art three CN201910804907.6 discloses a construction method and a code generation method of a software project natural language programming interface NLI. The code generation method comprises the following steps: 1) encapsulating each of the software items as a primitive in the NLI, including: functional feature description of primitives, API call patterns, object parameters, and other parameters; 2) determining the abstract syntax tree node type corresponding to the primitive; each node type specifies child nodes and attributes owned by the type node; binding each sub-node and attribute of the node with a corresponding Java code text region, and generating an abstract syntax tree of a code by a projection editor in the process of completing NLI primitive parameters; 3) and recursively converting each node from the root node of the abstract syntax tree, inserting the attributes and the child nodes in the abstract syntax tree nodes into the vacant part in the API calling mode, and finally generating the Java code corresponding to the primitive.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the first mode in the prior art is generally suitable for personal testing of algorithm personnel, and users in the mode can directly use docker service on a jupyter deployment machine, but generally use the docker service by a single user in order to ensure environmental stability, and a plurality of algorithm personnel cannot use the same jupyter service. So that the cost increases.
(2) In the prior art, a jupyter service provided by a second mode cloud manufacturer or kubeflow can support the creation of a plurality of jupyter services based on one platform, but a docker tool cannot be used in the jupyter, so that the generation efficiency of the mirror image is low.
The significance of solving the problems and the defects is as follows: the algorithm personnel can create enough jupyter containers for independent modeling on the basis of limited hardware resources, and can use a docker tool to perform mirroring and docker service deployment in the containers, and the integrated operation of debugging, constructing and deploying can be directly completed in the jupyter containers.
Disclosure of Invention
To address the problems of the prior art, the present invention provides a method, system, medium, and application for generating a mirror image in a machine learning component.
The invention is realized in such a way that a mirror image generation method used in a machine learning component is applied to a client, and the mirror image generation method used in the machine learning component comprises the following steps:
generating a new open source engine in a language programming environment created by a machine learning container;
the machine learning open source engine container is a notebook platform which is deployed based on a k8s cluster environment and is used for algorithmic personnel modeling, and a user can create an interactive development environment provided based on jupyter on the platform.
The main operation flow of the open source engine is as follows:
(1) logging in a notebook platform;
(2) creating a notebook service (selecting resources such as a jupyter mirror image to be used and a mounted data volume);
(3) entering into the created notebook service to carry out jupyter interactive development environment.
The process of starting the new open source engine container comprises the following steps: and debugging the codes in the language programming environment, and after the debugging of the codes is finished, compiling a dockerfile file and constructing the codes and the dependent environment into a docker mirror image.
The debugging part is mainly code debugging, and a user can execute relevant python operations such as model training, data preprocessing and the like in the jupyter interactive development environment.
The building part is mainly used for building a docker mirror image, and a user can compile a docker file in a jupyter interactive development environment based on a debugged code and build the docker mirror image for storage of the related code and the environment.
The deployment part is mainly used for creating a docker container to deploy and release services by utilizing the constructed docker mirror image.
Further, applied to the client, the mirror image generation method for use in the machine learning component further includes:
step one, installing a docker tool and generating a new jupyter container mirror image;
modifying the codes of the jupyter controller to enable the newly generated jupyter container to have a privileged mode;
and step three, starting the docker service in the newly-generated jupyter container, and using a docker tool to perform mirroring.
ps: and starting the docker service by using service docker start in the jupitter container.
Further, in the first step, a docker tool is installed on the basis of the jupyter mirror image.
Further, in the second step, the controller modification method is to modify the template file referred to when the controller creates a service, add the code of the relevant part of the privilege mode, and construct the modified code as a new notebook controller image for use. Using privileged mode may allow the container to use most of the capabilities on the host, including some kernel features and device access, similar to system level tools like systemctl service.
Further, in the third step, the docker service is started on the condition that the newly generated jupyter container has the privileged mode.
Another object of the present invention is to provide a mirror generation system for use in a machine learning component for the mirror generation method for use in a machine learning component, the mirror generation system for use in a machine learning component comprising:
(1) and the basic environment module supports the basic environment using the system to be an open source kubernets cluster + notebook service environment.
(2) And the new programming language program mirror image module is used for installing a docker tool on the basis of the original jupite program mirror image to generate a new jupiter mirror image. The container created by the mirror image can be used for calling a dockee tool in the jupyter program to execute docker mirror image printing, creating container service and other related operations, and the core technical point of the system is docker in docker.
(3) The programming language controller module is used for modifying bottom layer codes based on an original notebook controller, a container privilege mode module is added, a juptyter container created by the modified notebook controller can carry a privilege mode, and system layer commands such as systemctl service and the like can be used in the juptyter container.
(4) And the open source engine starting module, the new programming language program mirror module and the programming language controller module support the starting of the docker service in the jupitter container under the condition that the two modules meet the condition, and then use a docker tool. The image generation method for use in a machine learning component is performed.
Another object of the present invention is to provide a client for implementing the image generation method for use in a machine learning component.
The invention also aims to provide an application of the mirror image generation method used in the machine learning component in software development in the fields of finance, transportation and public safety.
By combining all the technical schemes, the invention has the advantages and positive effects that:
the Kubeflow is a set of technology stacks running on K8S, the technology stacks comprise a plurality of components, the relationship between the components is loose, and we can cooperate with each other or use a part of the components separately, a jupitter (a combination of (Julia a scientific computing-oriented high-performance dynamic high-level programming language), Python and R (a statistical analysis, drawing language and an operating environment) of a container platform is similar to a Jupiter (Jupiter), and the language supported by the Jupiter now is far beyond three, more than 40 programming languages such as C + +, C #, MATLAB, Spark (Scaa) and the like are supported by the Jupiter), a docker tool is used in an environment, and all jupitter environments created by the platform can be directly mirrored by the docker tool.
The invention starts the docker container and other related operations, and solves the operation requirements of algorithm personnel on debugging, constructing and deploying one-stop in the jupitter environment.
Compared with the prior art, after completing code debugging in jupyter, an algorithm worker needs to transfer the code to a server using docker service, then performs mirror image construction, and then uses the marked mirror image to deploy service verification effect. The invention enables an algorithm worker to directly use the docker service to image in the current jupyter environment after debugging the code and to deploy the one-stop requirement of the docker service, thereby avoiding the problem that the code is migrated and the service can be issued only by switching different platforms back and forth.
Drawings
FIG. 1 is a flowchart of a mirror generation method for use in a machine learning component according to an embodiment of the present invention.
Fig. 2 is an effect diagram of a mirror image generation method used in a machine learning component according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of an image generation system for use in a machine learning component according to an embodiment of the present invention;
in the figure: 1. a base environment module; 2. a new programming language program mirror module; 3. a programming language controller module; 4. and the open source engine starting module.
Fig. 4 is a comparison effect diagram of the mainstream scheme in the Jupyter modeling industry provided by the embodiment of the invention.
Fig. 5 is an operation flowchart of an image generation method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a mirror image generation method for use in a machine learning component, and the following describes the present invention in detail with reference to the accompanying drawings.
The invention can use docker (open source application container engine) in jupyter; when using the jupyter environment, an algorithm worker uses docker to package the service code and the environment of the algorithm worker into a mirror image, and starts a container at the jupyter to verify the scene of the mirror image function.
The machine learning open source engine container is a notebook platform which is deployed based on a k8s cluster environment and is used for algorithmic personnel modeling, and a user can create an interactive development environment provided based on jupyter on the platform.
The main operation flow of the open source engine is as follows:
1. logging in a notebook platform;
2. creating a notebook service (selecting resources such as a jupyter mirror image to be used and a mounted data volume);
3. entering into the created notebook service to carry out jupyter interactive development environment. As shown in fig. 1, the mirror image generation method for use in a machine learning component provided by the embodiment of the present invention includes the following steps:
s101, a docker tool is installed on the basis of the original jupyter mirror image, and a new jupyter mirror image is generated.
S102, modifying based on the original jupyter controller code to enable the newly generated jupyter container to have a privilege mode.
S103, using a service docker start command to start docker service in the jupitter container, and further using a docker tool.
The method for modifying the jupyter controller is characterized in that a template file quoted when the controller is modified to create service is added with codes of relevant parts of a privilege mode, and the modified codes are constructed into a new notebook controller mirror image for use. Using privileged mode may allow the container to use most of the capabilities on the host, including some kernel features and device access, similar to system level tools like systemctl service.
And starting the docker service under the condition that the newly generated jupyter container has a privileged mode.
Those skilled in the art of the mirror image generation method for machine learning components provided by the present invention can also implement other steps, and the mirror image generation method for machine learning components provided by the present invention of fig. 1 is only a specific embodiment.
In an embodiment of the present invention, fig. 2 is an effect diagram of a mirror image generation method for use in a machine learning component according to an embodiment of the present invention.
In an embodiment of the present invention, as shown in fig. 3, a mirroring generation system for use in a machine learning component provided in an embodiment of the present invention includes:
the basic environment module 1 is used for supporting the service environment using the basic environment as an open source kubernets cluster and a notebook;
the new programming language program mirror image module 2 is used for installing a docker tool on the basis of the original jupyter mirror image; generating a new jupyter mirror image; calling a docker tool in the jupyter program to execute docker mirroring by using the container created by the mirror image, and creating related operations of container service;
the programming language controller module 3 is used for modifying bottom layer codes based on an original notebook controller, adding a container privilege mode module, creating a jupyter container obtained by the modified notebook controller to carry a privilege mode, and using a systemct service in the jupyter container, wherein the command is two system layer commands used in the scope of the privilege mode function;
and the open source engine starting module 4 is used for supporting the mirror image to start the docker service in the jupitter container under the condition that the new programming language program mirror image module and the programming language controller module both meet the conditions, and further using a docker tool.
The embodiment of the invention has good effect, and compared with the current mainstream scheme, the embodiment of the invention has the following comparison:
the results of comparing the mainstream protocols in the Jupyter modeling industry are shown in Table 1.
TABLE 1Jupyter modeling industry mainstream schema comparison
| Jupyter deployment mode | Supporting template creation | Supporting the use of docker tools | Supporting multi-tenant usage |
| Server | Whether or not | Is that | Whether or not |
| K8s cluster | Whether or not | Is that | Whether or not |
| Cloud manufacturer | Is that | Whether or not | Is that |
| kubeflow | Is that | Whether or not | Is that |
| The invention | Is that | Is that | Is that |
The graph of the comparative effect is shown in fig. 4.
The operation flowchart of this embodiment is shown in fig. 5.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The mirror image generation method for the machine learning component is applied to a client, and comprises the following steps:
generating a new open source engine in a language programming environment created by a machine learning container;
starting a new open source engine service, debugging, constructing and deploying a one-stop operation flow in a language programming environment, and performing mirroring and deploying a docker container.
2. The image generation method for use in a machine learning component of claim 1,
the main operation flow of the open source engine is as follows:
(1) logging in a notebook platform;
(2) creating a notebook service, selecting a jupyter mirror image to be used, and mounting data volume resources;
(3) entering into the created notebook service to carry out jupyter interactive development environment.
3. The image generation method for use in a machine learning component of claim 1, wherein the main operational flow for starting a new open source engine container is: and debugging the codes in the language programming environment, and after the debugging of the codes is finished, compiling a dockerfile file and constructing the codes and the dependent environment into a docker mirror image.
4. The image generation method for machine learning components as claimed in claim 1, wherein the debugging part is mainly code debugging, and the user can perform model training and data preprocessing related python operations in jupyter interactive development environment;
the building part is mainly used for building a docker mirror image, and a user can compile a docker file in a jupyter interactive development environment based on a debugged code and build the docker mirror image for storage of the related code and the environment;
the deployment part is mainly used for creating a docker container to deploy and release services by utilizing the constructed docker mirror image.
5. The image generation method for use in a machine learning component of claim 1, applied to a client, further comprising:
step one, installing a docker tool; generating a new jupyter container;
modifying the codes of the jupyter controller to enable the newly generated jupyter container to have a privileged mode;
and step three, starting the docker service in the newly-generated jupyter container, and using a docker tool to perform mirroring.
6. The image generation method for use in a machine learning component of claim 5, wherein in step one, a docker tool is installed on the basis of a jupyter image; in the third step, the docker service is started under the condition that the newly generated jupyter container has the privileged mode.
7. An image generation system for a machine learning component, which implements the image generation method for the machine learning component according to any one of claims 1 to 6, and is applied to a client, the image generation system for the machine learning component comprises:
the basic environment module supports that the basic environment using the system is an open source kubernets cluster + notebook service environment;
the new programming language program mirror image module is used for installing a docker tool on the basis of the original jupite program mirror image to generate a new jupiter mirror image; a container created by the mirror image can be used for calling a dockee tool in the jupyter program to execute docker mirror image printing and creating container service related operations;
the programming language controller module is used for modifying bottom codes based on the original notebook controller, a container privilege mode module is added, a juptyter container created by the modified notebook controller can carry a privilege mode, and a systemctl service system layer command can be used in the juptyter container;
and the open source engine starting module, the new programming language program mirror module and the programming language controller module support the starting of the docker service in the jupitter container under the condition that the two modules meet the condition, and then use a docker tool.
8. A computer arrangement comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to carry out the image generation method for use in a machine learning component of any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the image generation method for use in a machine learning component of any one of claims 1 to 4.
10. A client, characterized in that, the client is used to implement the mirror image generation method for use in the machine learning component of any one of claims 1 to 4.
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| CN115390819A (en) * | 2022-10-28 | 2022-11-25 | 南京国睿信维软件有限公司 | Method and system for deeply integrating online drawing components by rich text editor |
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| CN115390819A (en) * | 2022-10-28 | 2022-11-25 | 南京国睿信维软件有限公司 | Method and system for deeply integrating online drawing components by rich text editor |
| CN115390819B (en) * | 2022-10-28 | 2023-08-22 | 南京国睿信维软件有限公司 | Method and system for deep integration of online drawing component of rich text editor |
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