CN117311784A - AI algorithm container visual manufacturing method, system, medium and equipment - Google Patents
AI algorithm container visual manufacturing method, system, medium and equipment Download PDFInfo
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Abstract
The invention discloses an AI algorithm container visual manufacturing method, an AI algorithm container visual manufacturing system, media and equipment, wherein the AI algorithm container visual manufacturing method comprises the following steps: acquiring a target AI algorithm of a target operation environment to be built; building a target operating environment for the target AI algorithm based on a preset operating environment building method and a software warehouse; converting the software dependence required in the process of building the target operating environment into a mirror image and uploading the mirror image to a software warehouse; therefore, the problem that the AI algorithm operation environment is high in knowledge threshold building and complicated in intranet installation and configuration can be solved.
Description
Technical Field
The invention relates to the technical field of construction of operation environments, in particular to an AI algorithm container visual manufacturing method, an AI algorithm container visual manufacturing system, an AI algorithm container visual manufacturing medium and AI algorithm container visual manufacturing equipment.
Background
Generally, when an AI algorithm engineer builds an algorithm running environment, there are several problems:
1. if the mode of manually building the operation environment on the server is adopted, the main operation is performed in the Linux environment, so that the participation of Linux engineering personnel is needed; if the AI algorithm Docker mirror image is adopted to make and then run on the server, docker, dockerfile engineering personnel are needed to participate, and the configuration environment is a complex and error-prone process, and is high in knowledge and operation threshold. Therefore, a set of complete algorithm operation environment is often built by matching multiple persons, and the operation environment is low in building efficiency.
2. In the configuration and installation process of the running environment, more dependent software is required to be downloaded and installed by connecting an external network, and in the intranet environment of an enterprise, the installed software is required to apply for the authority of resource site access, and the required software resource can be downloaded after the installation software passes through, so that the dependent application process of the installed software can be subjected to multiple authorities, and the working efficiency of AI algorithm personnel is greatly influenced.
Therefore, it is necessary to design an AI algorithm container visualization scheme to solve the above problems.
Disclosure of Invention
The invention provides a visual manufacturing method, a visual manufacturing system, a visual manufacturing medium and visual manufacturing equipment for an AI algorithm container, which can solve the problems that the AI algorithm running environment is high in building knowledge threshold and complicated in intranet installation and configuration.
In a first aspect, an AI algorithm container visualization method is provided, which specifically includes the following steps:
acquiring a target AI algorithm of a target operation environment to be built;
building a target operating environment for the target AI algorithm based on a preset operating environment building method and a software warehouse;
and converting the software dependence required in the process of building the target running environment into a mirror image and uploading the mirror image to a software warehouse.
According to a first aspect, in a first possible implementation manner of the first aspect, the step of building the target operating environment for the target AI algorithm based on a preset operating environment building method and a software warehouse specifically includes the following steps:
constructing an initial running environment;
inquiring the software dependence required by building the target running environment in a software warehouse;
and building a target operation environment of the target AI algorithm based on the initial operation environment and according to the queried software dependence.
In a first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the step of "building an initial running environment" specifically includes the following steps:
and selecting a resource server for constructing an operating environment by the target AI algorithm, confirming the GPU version on the resource server, and confirming the auxiliary function version of the GPU version.
In a second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, the auxiliary function version includes: NVIDIA driver version, CUDA version, CUDNN version and deep learning training frame library version.
According to the first aspect, in a fourth possible implementation manner of the first aspect, the step of converting software dependencies required in the process of building the target operating environment into a mirror image specifically includes the following steps:
acquiring a Dockerfile file required by converting the mirror image;
setting up a mirror image environment by calling a mirror image interface according to the Dockerfile file;
and converting software dependence required in the process of building the target running environment into a mirror image based on the mirror image environment.
In a second aspect, there is also provided an AI algorithm container visualization production system, including:
the acquisition module is used for acquiring a target AI algorithm of a target operation environment to be built;
the operation environment construction module is in communication connection with the acquisition module and is used for constructing a target operation environment for the target AI algorithm based on a preset operation environment construction method and a software warehouse; the method comprises the steps of,
and the mirror image uploading module is in communication connection with the running environment building module and is used for converting software dependence required in the process of building the target running environment into a mirror image and uploading the mirror image to a software warehouse.
In a third aspect, there is also provided a storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the AI-algorithm container visualization production method as described above.
In a fourth aspect, there is further provided an electronic device, including a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, where the processor implements the method for making the AI algorithm container visualizations as described above when running the computer program.
Compared with the prior art, the invention has the following advantages: by the aid of a preset operation environment construction method, a front-end visual installation interface is provided, detailed installation steps are guided, and therefore AI algorithm personnel can smoothly finish operation environment construction and algorithm mirror image construction of a target AI algorithm on the premise that Linux and Dockerfile engineering personnel are not involved, difficulty in construction of an environment and operation threshold are greatly reduced, time cost of AI algorithm development is reduced, and working efficiency of AI algorithm personnel in training environment configuration is improved. Meanwhile, the software warehouse is updated and managed, ready-made software version dependence is provided, local uploading and resource downloading sharing are supported, and compared with the situation that an AI algorithm worker can access a software resource site only after applying permission in an enterprise intranet each time, the situation that the environment is lost after the environment is used up is greatly shortened, the environment software dependence installation time is greatly shortened, the environment mirror image sharing utilization rate is improved, useless repeated work is avoided, and the output efficiency of the AI algorithm worker is greatly improved.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for manufacturing an AI algorithm container visualization in accordance with the present invention;
FIG. 2 is a flow chart of yet another embodiment of the AI algorithm container visualization method of the invention;
fig. 3 is a schematic structural diagram of an AI algorithm container visualization manufacturing system of the present invention.
Detailed Description
Reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with the specific embodiments, it will be understood that they are not intended to limit the invention to the described embodiments. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein may be implemented by any functional block or arrangement of functions, and any functional block or arrangement of functions may be implemented as a physical entity or a logical entity, or a combination of both.
The present invention will be described in further detail below with reference to the drawings and detailed description for the purpose of enabling those skilled in the art to understand the invention better.
Note that: the examples to be described below are only one specific example, and not as limiting the embodiments of the present invention necessarily to the following specific steps, values, conditions, data, sequences, etc. Those skilled in the art can, upon reading the present specification, make and use the concepts of the invention to construct further embodiments not mentioned in the specification.
Referring to fig. 1, an embodiment of the present invention provides a visual manufacturing method for an AI algorithm container, which is characterized by specifically including the following steps:
s100, acquiring a target AI algorithm of a target operation environment to be built;
this AI algorithm is typically some project file item written in the python language.
S200, building a target operation environment for the target AI algorithm based on a preset operation environment building method and a software warehouse;
s300, converting software dependence required in the process of building the target running environment into a mirror image, and uploading the mirror image to a software warehouse.
By converting the software dependencies required in the process of building the target operating environment into images and uploading the images to the software warehouse, the software warehouse is used for carrying out update management, the software warehouse supports automatic detection of software version update, a user can inquire whether the software dependencies required for installing the target operating environment exist in the software warehouse through an interface, and if the software dependencies do not exist, the software dependencies can be uploaded locally or downloaded online, because the software warehouse has larger network authority. After the current algorithm image is manufactured, the user can upload the current algorithm image to a warehouse for storage, and other users can inquire and download the needed image through the warehouse.
Specifically, in the embodiment, through the preset operation environment construction method, the front-end visual installation interface is provided, and detailed installation steps are guided, so that the operation environment construction of the target AI algorithm and the construction of the algorithm mirror image can be smoothly completed on the premise that no Linux and Dockerfire engineering personnel participate, the difficulty and the operation threshold of construction environment are greatly reduced, the time cost of AI algorithm development is reduced, and the working efficiency of AI algorithm personnel in training environment configuration is improved. Meanwhile, the software warehouse is updated and managed, ready-made software version dependence is provided, local uploading and resource downloading sharing are supported, and compared with the situation that an AI algorithm worker can access a software resource site only after applying permission in an enterprise intranet each time, the situation that the environment is lost after the environment is used up is greatly shortened, the environment software dependence installation time is greatly shortened, the environment mirror image sharing utilization rate is improved, useless repeated work is avoided, and the output efficiency of the AI algorithm worker is greatly improved.
Preferably, in another embodiment of the present application, the step of "S200, based on a preset operation environment building method and a software repository, building a target operation environment for the target AI algorithm specifically includes the following steps:
s210, constructing an initial running environment;
s220, inquiring software dependence required by building a target running environment in a software warehouse;
s230, building a target operation environment of the target AI algorithm based on the initial operation environment and according to the queried software dependence.
Preferably, in another embodiment of the present application, the step of "S210, constructing an initial running environment" specifically includes the following steps:
and selecting a resource server for constructing an operating environment by the target AI algorithm, confirming the GPU version on the resource server, and confirming the auxiliary function version of the GPU version.
Preferably, in a further embodiment of the present application, the auxiliary function version includes: NVIDIA driver version, CUDA version, CUDNN version and deep learning training frame library version.
Referring to fig. 2, specifically, in this embodiment, for constructing an initial running environment, the steps of constructing are: selecting a resource server constructed by an environment, prompting to confirm a GPU (graphic processor) version on the server, confirming an NVIDIA drive version (refer to a program for driving computer hardware, wherein the driver is a configuration file written by a hardware manufacturer according to an operating system, the hardware in the computer cannot work without the driver), a CUDA version (a CUDA tool kit is a C language development environment aiming at a GPU supporting the CUDA function), a CUDNN (a deep neural network library is a primitive library for the deep neural network accelerated by the GPU), confirming a deep learning training framework library version suitable for the current GPU version, inquiring a software dependent file (generally file name: request. Txt) required by building a target running environment in a software warehouse, and finally clicking the environment construction, wherein the system can automatically read file content to perform environment installation construction. The above completes the construction of the AI algorithm running environment visualization.
For each operation of the visual interface, the visual interface is converted into an instruction which can be identified by a system background, and for the software dependence needing to be installed, the system background automatically searches for matching in a software warehouse, and the software dependence which cannot be matched in the warehouse can be installed in a networking mode or give an interface prompt.
Preferably, in another embodiment of the present application, the step of "S300, converting software dependencies required in the process of building the target operating environment into images" specifically includes the following steps:
s310, acquiring a Dockerfile file required by a conversion mirror image;
dockerfile is a text file used for constructing a Docker mirror image, and the text content contains a piece of instructions, parameters and descriptions required for constructing the mirror image; instructions such as RUN, CMD, FROM, EXPOSE, ENV may be used in the Docker file.
S320, building a mirror image environment according to the Dockerf file and calling a mirror image interface;
s330, converting software dependence required in the process of building the target running environment into a mirror image based on the mirror image environment.
Referring to fig. 2, specifically, in this embodiment, after the target operating environment is built for the target AI algorithm, a dockerin file required for building a mirror image is generated, at this time, the mirror image environment may be built by calling the Docker Engine API interface and using the generated dockerin file, and finally, based on the mirror image environment, software dependency required in the process of building the target operating environment is converted into a mirror image.
Referring to fig. 3, the embodiment of the invention also provides an AI algorithm container visualization manufacturing system, which includes:
the acquisition module is used for acquiring a target AI algorithm of a target operation environment to be built;
the operation environment construction module is in communication connection with the acquisition module and is used for constructing a target operation environment for the target AI algorithm based on a preset operation environment construction method and a software warehouse; the method comprises the steps of,
and the mirror image uploading module is in communication connection with the running environment building module and is used for converting software dependence required in the process of building the target running environment into a mirror image and uploading the mirror image to a software warehouse.
Therefore, the invention has the following gain effects: firstly, through the visual installation step and command encapsulation conversion, the AI algorithm personnel can smoothly complete the construction of the algorithm mirror image on the premise of not participating in Linux and Dockerfile engineering personnel, the difficulty and the operation threshold of the construction environment are greatly reduced, the time cost of AI algorithm development is reduced, and the working efficiency of the AI algorithm personnel in the configuration of the training environment is improved. Second, through the update and management to the system software warehouse, provide the ready software version dependence, support local uploading and resource download share, compare in AI algorithm personnel each time apply for the authority in the enterprise intranet and can visit the software resource website, will lose these circumstances after the environment is used up, has shortened the time that the environment software relies on the installation greatly, has improved the environment mirror image and shared the utilization ratio, avoided some useless repetitive work, has greatly improved AI algorithm personnel's output efficiency.
Specifically, the present embodiment corresponds to the foregoing method embodiments one by one, and the functions of each module are described in detail in the corresponding method embodiments, so that a detailed description is not given.
Based on the same inventive concept, the embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements all or part of the method steps of the above method.
The present invention may be implemented by implementing all or part of the above-described method flow, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which when executed by a processor, may implement the steps of the above-described method embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
Based on the same inventive concept, the embodiments of the present application further provide an electronic device, including a memory and a processor, where the memory stores a computer program running on the processor, and when the processor executes the computer program, the processor implements all or part of the method steps in the above method.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control center of the computer device, and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (e.g., a sound playing function, an image playing function, etc.); the storage data area may store data (e.g., audio data, video data, etc.) created according to the use of the handset. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, server, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), servers and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. The visual manufacturing method of the AI algorithm container is characterized by comprising the following steps of:
acquiring a target AI algorithm of a target operation environment to be built;
building a target operating environment for the target AI algorithm based on a preset operating environment building method and a software warehouse;
and converting the software dependence required in the process of building the target running environment into a mirror image and uploading the mirror image to a software warehouse.
2. The visual manufacturing method of AI algorithm container according to claim 1, wherein the step of constructing a target operating environment for the target AI algorithm based on a preset operating environment construction method and a software warehouse specifically comprises the steps of:
constructing an initial running environment;
inquiring the software dependence required by building the target running environment in a software warehouse;
and building a target operation environment of the target AI algorithm based on the initial operation environment and according to the queried software dependence.
3. The AI algorithm container visualization production method of claim 2, wherein the step of constructing an initial operating environment comprises the steps of:
and selecting a resource server for constructing an operating environment by the target AI algorithm, confirming the GPU version on the resource server, and confirming the auxiliary function version of the GPU version.
4. The AI algorithm container visualization production method of claim 3, wherein the auxiliary function version comprises: NVIDIA driver version, CUDA version, CUDNN version and deep learning training frame library version.
5. The AI algorithm container visualization production method according to claim 1, wherein the step of converting software dependencies required in the process of building the target running environment into mirror images specifically comprises the steps of:
acquiring a Dockerfile file required by converting the mirror image;
setting up a mirror image environment by calling a mirror image interface according to the Dockerfile file;
and converting software dependence required in the process of building the target running environment into a mirror image based on the mirror image environment.
6. An AI algorithm container visualization production system, comprising:
the acquisition module is used for acquiring a target AI algorithm of a target operation environment to be built;
the operation environment construction module is in communication connection with the acquisition module and is used for constructing a target operation environment for the target AI algorithm based on a preset operation environment construction method and a software warehouse; the method comprises the steps of,
and the mirror image uploading module is in communication connection with the running environment building module and is used for converting software dependence required in the process of building the target running environment into a mirror image and uploading the mirror image to a software warehouse.
7. A storage medium having stored thereon a computer program which, when executed by a processor, implements the AI algorithm container visualization production method of any of claims 1 to 5.
8. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein the processor implements the AI-algorithm container visualization production method of any of claims 1-5 when the computer program is executed by the processor.
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| CN202311186069.3A CN117311784A (en) | 2023-09-12 | 2023-09-12 | AI algorithm container visual manufacturing method, system, medium and equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117950646A (en) * | 2024-03-26 | 2024-04-30 | 苏州元脑智能科技有限公司 | Software development method, device, computer equipment, storage medium and program product |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117950646A (en) * | 2024-03-26 | 2024-04-30 | 苏州元脑智能科技有限公司 | Software development method, device, computer equipment, storage medium and program product |
| CN117950646B (en) * | 2024-03-26 | 2024-06-07 | 苏州元脑智能科技有限公司 | Software development method, device, computer equipment, storage medium and program product |
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