Remote development method and device for intelligent computing center model oriented to popularity

Abstract

The invention provides an intelligent computing center model remote development method and device oriented to the common computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure. The method comprises the steps that under the condition that operation information is received, the operation environment of a first container is adjusted based on the operation information, and a target environment file is generated, the target environment file is used for representing related conditions of the operation environment; under the condition that a code adjustment instruction is received, a running environment of a second container is created based on the target environment file, and the second container is a container created based on CPU resources of the intelligent computing center; adjusting the model code in the operation environment of the second container based on the code adjustment instruction; and scheduling GPU resources of the intelligent computing center to process the adjusted model codes. According to the method, the computing power resource utilization rate of the intelligent computing center can be greatly improved, and the economic cost of model development is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent computing centers, smart computing centers and computing power infrastructure, and specifically to a remote development method and device for an intelligent computing center model for universal computing power. Background Art

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "intelligent computing centers" have come into being.

[0003] "Intelligent computing center" refers to a facility that provides the required computing power, data and algorithms for artificial intelligence applications (such as artificial intelligence deep learning model development, model training and model reasoning) by using large-scale heterogeneous computing resources, including general computing power and intelligent computing power. The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from bottom-level computing power to top-level application enablement.

[0004] “Intelligent Computing Center” includes but is not limited to “Smart Computing Center”.

[0005] "Intelligent Computing Center" refers to an artificial intelligence computing center. It is a type of computing infrastructure that is based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services, and algorithm services required for artificial intelligence applications.

[0006] "Computing power" is the core of "intelligent computing center" and "intelligent computing center". It is the ability of computer equipment or computing / data center to process information. It is the ability of computer hardware and software to work together to execute certain computing needs. It is the computing power to process information data and achieve target result output. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity. It mainly provides services to society through computing power infrastructure.

[0007] At present, the intelligent computing center provides computing resources to users through graphics processing unit (GPU) accelerator cards, so that users can develop models based on the computing resources of the intelligent computing center and reduce the cost of model development. However, in the existing technology, the model development process is relatively long, and users need to frequently adjust the model during the development process. The adjustment process still needs to occupy the computing resources of the intelligent computing center, resulting in the long-term occupation of the computing resources of the intelligent computing center and the low utilization rate of computing resources. At the same time, users need to rent the computing services of the intelligent computing center for a long time, resulting in high cost of model development, making it difficult to achieve widespread application of inclusive computing power.

[0008] It can be seen that the existing technology has the problems of low utilization of computing resources and high cost of model development. Summary of the invention

[0009] The present invention provides a remote development method and device for an intelligent computing center model for universal computing power, so as to solve the problems in the prior art of low utilization of computing power resources and high cost of model development.

[0010] To solve the above problems, the present invention is achieved as follows: In a first aspect, the present invention provides a remote development method of an intelligent computing center model for inclusive computing power, comprising: Step S1: When operation information is received, the operating environment of the first container is adjusted based on the operation information, and a target environment file is generated, where the target environment file is used to characterize relevant conditions of the operating environment, and the first container is a container created based on the central processing unit CPU resources of the intelligent computing center; Step S2: when receiving the code adjustment instruction, creating an operating environment of a second container based on the target environment file, where the second container is a container created based on the CPU resources of the intelligent computing center; Step S3: adjusting the model code in the running environment of the second container based on the code adjustment instruction; Step S4: dispatching the graphics processor GPU resources of the intelligent computing center to process the adjusted model code.

[0011] In one embodiment, step S1 includes: Step S11, obtaining an initial environment file, wherein the initial environment file is a file corresponding to the container when the model code is last adjusted; Step S12: when receiving the operation information, adjusting the operating environment of the first container based on the operation information to obtain an adjustment result; Step S13: When the adjustment result meets the first preset condition, the adjustment result is saved in layers to the initial environment file to obtain the target environment file.

[0012] In one embodiment, the first preset condition includes at least one of the following: The data volume of the adjustment result is greater than or equal to a first set data threshold; The time since the last adjustment result was saved is greater than or equal to the set time threshold.

[0013] In one embodiment, the initial environment file includes at least one storage layer, and step S13 includes: Step S131, traversing at least one storage layer of the initial environment file; Step S132: determining a target preservation layer, wherein the target preservation layer is a preservation layer that satisfies a second preset condition among the at least one preservation layer; Step S133: Save the adjustment result to the target storage layer to obtain the target environment file.

[0014] In one embodiment, the second preset condition includes at least one of the following: The data volume of the target storage layer is less than or equal to a second set data threshold; The sum of the data volume of the target storage layer and the data volume of the adjustment result is less than or equal to a third set data threshold; The number of layers of the target preservation layer is less than or equal to a set number threshold.

[0015] In one embodiment, step S1 includes: Step S14: when the operation information is received and a save instruction is received, the operating environment of the first container is adjusted based on the operation information to obtain an adjustment result; Step S15: Save the multiple processes of the first container after adjusting the running environment and the memory information corresponding to each process to obtain the target environment file, wherein at least one process among the multiple processes is a process corresponding to the adjustment result.

[0016] In one embodiment, step S2 includes at least one of the following: Step S21: When the first container and the second container satisfy the affinity rule, affinity scheduling is performed on the target environment file, and a running environment of the second container is created based on the target environment file obtained by scheduling; Step S22: If the first container and the second container do not satisfy the affinity rule, schedule the target environment file based on a peer-to-peer P2P network, and create a running environment for the second container based on the scheduled target environment file.

[0017] In one embodiment, step S4 includes: Step S41: Create a third container based on the GPU resources of the intelligent computing center; Step S42: Processing the adjusted model code based on the third container to obtain a processing result; After step S4, the method further includes: Step S5: Send the processing result to the terminal, close the third container, and release the GPU resources of the intelligent computing center occupied by the third container.

[0018] In a second aspect, the present invention further provides a remote development device for an intelligent computing center model for universal computing power, comprising: A generation module, configured to adjust the operating environment of the first container based on the operating information when the operating information is received, and generate a target environment file, wherein the target environment file is used to characterize relevant conditions of the operating environment, wherein the first container is a container created based on the central processing unit (CPU) resources of the intelligent computing center; A creation module, configured to create an operating environment of a second container based on the target environment file when a code adjustment instruction is received, wherein the second container is a container created based on the CPU resources of the intelligent computing center; An adjustment module, configured to adjust the model code in the running environment of the second container based on the code adjustment instruction; The processing module is used to schedule the graphics processor GPU resources of the intelligent computing center to process the adjusted model code.

[0019] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps in the remote development method of an intelligent computing center model for inclusive computing power as described in the first aspect above are implemented.

[0020] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the remote development method of an intelligent computing center model for inclusive computing power as described in the first aspect above are implemented.

[0021] In a fifth aspect, the present invention further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps in the remote development method of an intelligent computing center model for inclusive computing power as described in the first aspect above.

[0022] In the present invention, when operation information is received, the operating environment of the first container is adjusted based on the operation information, and a target environment file is generated, the target environment file is used to characterize the relevant conditions of the operating environment, the first container is a container created based on the central processing unit CPU resources of the intelligent computing center; when a code adjustment instruction is received, the operating environment of the second container is created based on the target environment file, the second container is a container created based on the CPU resources of the intelligent computing center; the model code is adjusted in the operating environment of the second container based on the code adjustment instruction; the graphics processor GPU resources of the intelligent computing center are scheduled to process the adjusted model code. In this way, by generating a target environment file, the CPU resources are occupied to create a second container when a code adjustment instruction is received, and the operating environment of the second container is created through the target environment file, so that the user can adjust the model code in the second container through the code adjustment instruction without occupying the computing power resources of the intelligent computing center, which greatly improves the utilization rate of the computing power resources of the intelligent computing center. Furthermore, since users do not need to rent the computing power service of the intelligent computing center for a long time, the economic cost of model development for users is greatly reduced, and the wide application of inclusive computing power is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0024] Figure 1 It is a flow chart of a remote development method of an intelligent computing center model for inclusive computing power provided by the present invention; Figure 2 It is a structural diagram of a remote development device for an intelligent computing center model for universal computing power provided by the present invention; Figure 3 It is a structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The "computing power" mentioned in the present invention refers to: the ability of computer equipment or computing / data center to process information, the ability of computer hardware and software to work together to execute certain computing requirements, and the computing power to achieve target result output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity, and it mainly provides services to the society through computing power infrastructure.

[0027] The "computing power" (Computational Power, CP) mentioned in the present invention refers to: it is the ability of a data center server to process data and output results. It is a comprehensive indicator to measure the computing power of a data center, including general computing power, super computing power and intelligent computing power. The commonly used unit of measurement is the number of floating-point operations performed per second (FLOPS, 1EFLOPS=10^18FLOPS). The larger the value, the stronger the comprehensive computing power. According to calculations, 1 EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream notebooks. The calculation formula is: CP=CP 通用 +CP 智能 +CP 超级 .

[0028] The "carrying capacity" (Network Power, NP) mentioned in the present invention refers to: it is the performance of the data transmission capacity of the computing power facilities, including the comprehensive capabilities of network architecture, network bandwidth, transmission delay, intelligent management and scheduling, etc. It involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling capabilities.

[0029] The "Storage Power" (SP) mentioned in the present invention refers to: the comprehensive capabilities of a data center in terms of data storage capacity, performance, safety and reliability, and green and low-carbon. It is a comprehensive indicator for measuring the data storage capacity of a data center, including external storage devices such as storage arrays and server built-in storage devices. The commonly used unit of measurement for storage capacity is exabyte (EB, 1EB=2^60bytes), and the commonly used unit of measurement for performance is the number of reads and writes per second per unit capacity (IOPS / TB, Input / Output Operations Per Second / TB). The disaster recovery ratio is an important manifestation of safety and reliability.

[0030] The "computing power infrastructure" mentioned in the present invention refers to: a new type of information infrastructure that integrates information computing power, network carrying capacity, and data storage capacity, which can realize centralized computing, storage, transmission and application of information.

[0031] The "new information infrastructure" mentioned in the present invention refers to: mainly including network infrastructure such as 5G networks, fiber-optic broadband networks, backbone networks, international communication networks, satellite Internet, computing power infrastructure such as data centers, general computing power centers, intelligent computing centers, supercomputing centers, and new technology facilities such as artificial intelligence, blockchain, and quantum computing.

[0032] The “computing power” mentioned in the present invention includes: general computing power, intelligent computing power and super computing power.

[0033] The “general computing power” mentioned in the present invention refers to: the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0034] The "intelligent computing power" mentioned in the present invention refers to: a computing platform based on GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit) and other dedicated chips for various innovative artificial intelligence applications, such as natural language processing, machine vision, etc.

[0035] The "super computing power" mentioned in the present invention refers to: the computing power mainly provided by high-performance computing clusters such as supercomputers. It uses the centralized computing resources of multiple computer systems working in parallel and uses a dedicated operating system to handle extremely complex or data-intensive problems. It is mainly used for calculations in cutting-edge scientific fields, such as planetary simulation, drug molecule design, gene analysis, etc.

[0036] The "intelligent computing center" mentioned in the present invention refers to a facility that provides the required computing power, data and algorithms for artificial intelligence applications (such as artificial intelligence deep learning model development, model training and model reasoning) by using large-scale heterogeneous computing resources, including general computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from bottom-level computing power to top-level application enablement.

[0037] The “intelligent computing center” mentioned in the present invention includes but is not limited to the “intelligent computing center”.

[0038] The "intelligent computing center" mentioned in the present invention is an artificial intelligence computing center, which is a type of computing power infrastructure that is based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services, and algorithm services required for artificial intelligence applications.

[0039] The "computing power center" mentioned in the present invention refers to: a facility that is mainly composed of infrastructure such as wind, fire, water, electricity, and IT hardware and software equipment, and has computing power, transportation capacity, and storage capacity, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0040] The "supercomputing center" mentioned in the present invention refers to: a supercomputing data center, which is a data center based on a supercomputer or a large-scale computing cluster, which can provide functions such as large-scale computing, storage and network services, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling and genome sequencing.

[0041] The "computing resources" mentioned in the present invention refer to: technologies and facilities with information computing, transmission, storage and application capabilities required for the development of a digital society, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and supporting and guarantee resources such as wind, fire, water and electricity.

[0042] The "inclusive computing power" mentioned in the present invention refers to providing appropriate and effective computing power services at an affordable cost to all social classes and groups that have computing power service needs based on the requirements of equal opportunity and the principle of commercial sustainability.

[0043] The “model” mentioned in the present invention includes but is not limited to a “large language model” and a “multimodal large model”.

[0044] The “large language model” mentioned in the present invention refers to a large language model (LLM), which is a language model with a large parameter scale, designed to understand and generate human language, trained with a large amount of text data, and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0045] The "Multimodal Large Models" mentioned in the present invention refer to models that combine multimodal information such as text, images, videos, audio, etc. for training, including but not limited to multimodal large language models.

[0046] See also Figure 1 , Figure 1 is a flowchart of a remote development method of an intelligent computing center model for universal computing power provided by the present invention, such as Figure 1 As shown, the following steps are included: Step S1: When operation information is received, the operating environment of the first container is adjusted based on the operation information, and a target environment file is generated, where the target environment file is used to characterize relevant conditions of the operating environment. The first container is a container created based on the central processing unit (CPU) resources of the intelligent computing center.

[0047] The above operation information is information sent when the user needs to remotely adjust the operating environment of the first container. When the intelligent computing center receives the operation information, it parses the operation information to obtain the specific content of the adjustment to the operating environment, and then adjusts the operating environment of the first container through the specific content to obtain the adjustment result.

[0048] The operation information package is used to represent at least one operation, such as the installation, update or uninstallation of the development package of the operating environment, etc., which is an operation performed on the system.

[0049] The above adjustment result is the adjustment result obtained by adjusting the operating environment of the first container after the intelligent computing center receives the operation information. The adjustment result corresponds to the received operation information. When multiple operation information is received, the first container will be adjusted multiple times to obtain multiple adjustment results. The multiple adjustment results correspond to the multiple operation information one by one. By saving the multiple adjustment results, a target environment file is obtained, and the operating environment of the first container can be restored through the target environment file.

[0050] In some implementations, before the user sends the operation information, it is necessary to install the integrated development environment (IDE) software on the terminal, and send the operation information to the intelligent computing center through the IDE software to adjust the operating environment of the first container. The IDE software may be the code integrated development software or the industry standard IDE extension plug-in provided by the intelligent computing center, and the code integrated development software or the industry standard IDE extension plug-in may be used to create the operating environment and schedule the computing resources of the intelligent computing center to process the adjusted model code.

[0051] Specifically, the user logs in on the terminal, creates the operating environment of the first container through code integration development software or IDE extension plug-in, and adjusts the operating environment of the first container through operation information, so that the operating environment of the first container of the intelligent computing center meets the requirements of running the model code.

[0052] Furthermore, users can upload the model code and operating data that need to be adjusted to the storage layer of the intelligent computing center through the IDE software, or directly upload the model code and operating data that need to be adjusted to the second container through the IDE software, which improves the flexibility of model development.

[0053] The above-mentioned first container is a container created based on the CPU resources of the intelligent computing center. It should be noted that since the intelligent computing center provides computing resources to run the model code through the GPU accelerator card, and the number of GPU accelerator cards that can be deployed in the intelligent computing center is limited, it cannot provide computing resources unlimitedly. In order to reduce the occupation of the computing resources of the intelligent computing center during the model adjustment process, the first container is created based on CPU resources in the present invention, and the operating environment for the model operation is set in the first container, and the model code is remotely updated or adjusted in the first container, thereby reducing the occupation of the computing resources of the intelligent computing center, thereby achieving the improvement of the computing resource utilization rate of the intelligent computing center; further, since users do not need to rent the computing service of the intelligent computing center for a long time, the economic cost of model development for users is greatly reduced, and the widespread application of inclusive computing power is realized.

[0054] The operating environment of the first container is remotely created and adjusted by the user, specifically, the user sends operation information to the intelligent computing center, and the intelligent computing center creates and adjusts the operating environment of the first container based on the operation information.

[0055] The above-mentioned target environment file is used to characterize the relevant conditions of the operating environment of the first container. Based on the target environment file, the operating environment of the first container can be restored in other containers, so that the first container can be closed when the user does not need to adjust the model code, and a second container can be temporarily created when the user needs to adjust the model code, and the operating environment of the second container can be created based on the target environment file, so that the user can adjust the model code through the temporarily created second container.

[0056] Step S2: When a code adjustment instruction is received, an operating environment of a second container is created based on the target environment file, where the second container is a container created based on the CPU resources of the intelligent computing center.

[0057] The above code adjustment instruction is an instruction sent by the user when the model code needs to be adjusted. The code adjustment instruction can enable the user to remotely adjust the model code of the model deployed in the intelligent computing center.

[0058] The operating environment of the second container is created based on the target environment file. The environment of the second container is consistent with that of the first container, so that the second container can arrange the model code, and the user can remotely adjust the model code through the second container.

[0059] It should be noted that both the first container and the second container can enable users to remotely adjust the model code. However, since users may have other needs during the model development process, the model code cannot be adjusted continuously. In order to reduce the CPU resource usage of the intelligent computing center, when the model does not need to be adjusted, it is necessary to close the first container and then start the second container to enable users to remotely adjust the model code.

[0060] That is, in some implementations, when the first container is already running in the intelligent computing center, there is no need to create a second container. Instead, the model code is adjusted directly in the running environment of the first container based on the code adjustment instruction.

[0061] In some implementations, when the first container does not exist in operation in the first intelligent computing center, an operating environment of a second container is created based on the target environment file, and then the model code is adjusted in the operating environment of the second container based on the code adjustment instruction.

[0062] Step S3: adjusting the model code in the running environment of the second container based on the code adjustment instruction.

[0063] It should be noted that the model code is arranged in the second container, and the operating environment of the second container is the environment corresponding to the model code. Through the second container, the user can remotely adjust the model code of the second container.

[0064] Specifically, after the intelligent computing center receives the code adjustment instruction, it creates a second container, creates an operating environment for the second container based on the target environment file, and places the model code in the second container; parses the code adjustment instruction to obtain the code adjustment content; and adjusts the model code in the second container based on the code adjustment content.

[0065] In some embodiments, the model code is stored in a storage layer of an intelligent computing center. When the model code does not need to be adjusted, the container is closed and the model code is stored in the storage layer. At this time, the model code only occupies the storage resources of the intelligent computing center, and does not occupy CPU resources and GPU resources. When the model code needs to be adjusted, a second container is created, and the model code is obtained from the storage layer, and the model code is arranged in the second container. Then, the user adjusts the model code in the second container through code adjustment instructions, thereby improving the utilization rate of the CPU resources and GPU resources of the intelligent computing center.

[0066] Step S4: dispatching the graphics processor GPU resources of the intelligent computing center to process the adjusted model code.

[0067] After the model code is adjusted in the second container, the graphics processor GPU resources of the intelligent computing center are dispatched to process the adjusted model code, thereby avoiding long-term occupation of the computing power resources of the intelligent computing center and improving the utilization rate of the computing power resources of the intelligent computing center.

[0068] In the present invention, when operation information is received, the operating environment of the first container is adjusted based on the operation information, and a target environment file is generated, the target environment file is used to characterize the relevant conditions of the operating environment, the first container is a container created based on the central processing unit CPU resources of the intelligent computing center; when a code adjustment instruction is received, the operating environment of the second container is created based on the target environment file, the second container is a container created based on the CPU resources of the intelligent computing center; the model code is adjusted in the operating environment of the second container based on the code adjustment instruction; the graphics processor GPU resources of the intelligent computing center are scheduled to process the adjusted model code. In this way, by generating a target environment file, the CPU resources are occupied to create a second container when a code adjustment instruction is received, and the operating environment of the second container is created through the target environment file, so that the user can adjust the model code in the second container through the code adjustment instruction without occupying the computing power resources of the intelligent computing center, which greatly improves the utilization rate of the computing power resources of the intelligent computing center. Furthermore, since users do not need to rent the computing power service of the intelligent computing center for a long time, the economic cost of model development for users is greatly reduced, and the wide application of inclusive computing power is realized.

[0069] In one embodiment, step S1 includes: Step S11, obtaining an initial environment file, wherein the initial environment file is a file corresponding to the container when the model code is last adjusted; Step S12: when receiving the operation information, adjusting the operating environment of the first container based on the operation information to obtain an adjustment result; Step S13: When the adjustment result meets the first preset condition, the adjustment result is saved in layers to the initial environment file to obtain the target environment file.

[0070] The above-mentioned initial environment file is the file corresponding to the container when the model code was last adjusted, that is, the initial environment file is the latest environment file corresponding to the model code stored in the intelligent computing center. When operation information is received, the initial environment file needs to be updated so that the target environment file obtained after the update can obtain the updated operating environment.

[0071] It should be noted that when the environment file is created for the first time, the intelligent computing center pre-creates the first container, and then the user remotely creates the operating environment through operation information; specifically, after the intelligent computing center receives the operation information, the operating environment of the first container is adjusted to obtain an adjustment result. When the adjustment result meets the first preset condition, the adjustment result is saved to generate the first environment file. When new operation information is received subsequently, a new adjustment result is generated based on the new operation information, and the new adjustment result is continuously saved on the basis of the first environment file to obtain the target environment file, and the operating environment of the model code can be restored through the target environment file.

[0072] In the present invention, an initial environment file is obtained, and the initial environment file is a file corresponding to the container when the model code is last adjusted; when the operation information is received, the operating environment of the first container is adjusted based on the operation information to obtain an adjustment result; when the adjustment result meets the first preset condition, the adjustment result is saved in layers to the initial environment file to obtain the target environment file. In this way, the environment file is regularly updated through the first preset condition.

[0073] In one embodiment, the first preset condition includes at least one of the following: The data volume of the adjustment result is greater than or equal to a first set data threshold; The time since the last adjustment result was saved is greater than or equal to the set time threshold.

[0074] The above-mentioned first set data threshold is used to determine whether the data volume of the adjustment result meets the saving condition. It should be noted that in the process of users remotely adjusting the operating environment of the container, the operating environment may need to be adjusted frequently. At this time, if the adjustment result corresponding to the operation information is saved as the target environment file after receiving the operation information once, it will occupy more computing power resources. Therefore, the first set data threshold is set in the present invention, and whether to save the adjustment result is determined by whether the data volume of the adjustment result meets the first set data threshold.

[0075] For example, the first set data threshold is set to 100M, operation information is continuously received, and the operating environment is adjusted based on the received operation information to obtain an adjustment result; when the data volume of the received adjustment result reaches 100M, the adjustment results corresponding to all the received operation information are saved to the initial environment file to obtain the target environment file.

[0076] In addition to the above methods, a time threshold can also be set to determine whether the adjustment result needs to be saved. That is, in the present invention, in the process of continuously receiving operation information, the adjustment result corresponding to the operation information is saved once at a fixed interval by setting a time threshold, which can also reduce the computing power resources occupied by saving the adjustment result.

[0077] For example, the time threshold is set to 30s, operation information is continuously received, and the operating environment is adjusted based on the received operation information to obtain the adjustment result; when the time from the last time the adjustment result was saved reaches 30s, the adjustment results corresponding to all the received operation information are saved to the initial environment file to obtain the target environment file.

[0078] In the present invention, the adjustment results are saved periodically by setting a first set data threshold or a set time threshold, so as to avoid frequent saving of the adjustment results which would occupy more computing power resources of the intelligent computing center.

[0079] In one embodiment, the initial environment file includes at least one storage layer, and step S13 includes: Step S131, traversing at least one storage layer of the initial environment file; Step S132: determining a target preservation layer, wherein the target preservation layer is a preservation layer that satisfies a second preset condition among the at least one preservation layer; Step S133: Save the adjustment result to the target storage layer to obtain the target environment file.

[0080] It should be noted that, since the user's remote adjustment of the container's operating environment is a continuous process, in order to ensure the stability of the operating environment created by the environment file, the adjustment results are stored in layers in the present invention. Among them, the initial environment file includes at least one storage layer, each storage layer is used to store the adjustment results, and the adjustment results are saved in sequence through layered storage.

[0081] The target storage layer is a storage layer that meets the second preset condition. It should be noted that the initial environment file includes at least one storage layer, and the target storage layer that meets the second preset condition is determined by traversing at least one storage layer, and the adjustment result can be saved through the target storage layer.

[0082] The second preset condition includes at least one of the following: The data volume of the target storage layer is less than or equal to a second set data threshold; The sum of the data volume of the target storage layer and the data volume of the adjustment result is less than or equal to a third set data threshold; The number of layers of the target preservation layer is less than or equal to a set number threshold.

[0083] It should be noted that each storage layer of the target environment file cannot store the adjustment results infinitely, and the target environment file cannot store the adjustment results infinitely, so the present invention limits the amount of data stored in each storage layer and the number of layers of the target environment file.

[0084] Among them, the second set data threshold and the third set data threshold are used to limit the amount of data stored in each storage layer. The second set data threshold and the third set data threshold can be the same or different; the set quantity threshold is used to limit the number of layers of the target environment file.

[0085] For example, the second set data threshold and the third set data threshold are the same, both set to 10G, and the set quantity threshold is 128 layers, so that the size of each saved layer in the target environment file does not exceed 10G after saving the adjustment results, and the size of the target environment file does not exceed 1280G.

[0086] In the present invention, the second data threshold and the third data threshold are set to limit the amount of data stored in each storage layer, and the number threshold is set to limit the number of layers of the target environment file so that the target environment file does not occupy too much storage resources of the intelligent computing center.

[0087] In one embodiment, step S1 includes: Step S14: when the operation information is received and a save instruction is received, the operating environment of the first container is adjusted based on the operation information to obtain an adjustment result; Step S15: Save the multiple processes of the first container after adjusting the running environment and the memory information corresponding to each process to obtain the target environment file, wherein at least one process among the multiple processes is a process corresponding to the adjustment result.

[0088] It should be noted that, in addition to the above-mentioned solution of periodically saving the adjustment results, the environment information can also be saved immediately according to the user's save instruction. Since the staged adjustment of the operating environment is usually not completed when the user sends the save instruction, that is, a completed adjustment result has not been formed, and the adjustment result cannot be directly saved at this time, the process of the first container and the memory information corresponding to each process are directly saved in the present invention to obtain the target environment file, so that the process in the first container can be restored through the target environment file, and the user can continue to complete the adjustment of the operating environment on the basis of restoring the process.

[0089] Among them, the save instruction is an instruction sent by the user through the IDE software. When the save instruction is received, the intelligent computing center saves multiple processes of the first container and the memory information corresponding to each process as a target environment file. At this time, the target environment file is a binary file used to restore the container process.

[0090] Furthermore, since the first container is a container created based on CPU resources, the multiple processes do not include a GPU process, thereby reducing the size of the target environment file.

[0091] In one embodiment, step S2 includes at least one of the following: Step S21: When the first container and the second container satisfy the affinity rule, affinity scheduling is performed on the target environment file, and a running environment of the second container is created based on the target environment file obtained by scheduling; Step S22: If the first container and the second container do not satisfy the affinity rule, schedule the target environment file based on a point-to-point (P2P) network, and create a running environment for the second container based on the scheduled target environment file.

[0092] The above affinity rules are used to characterize the affinity between the first container and the second container. The affinity rules can specifically be whether the first container and the second container are located on the same node, or whether the first container and the second container use the computing resources of the same GPU. It should be noted that when the first container and the second container meet the affinity rules, the target environment file can be directly affinity-scheduled to quickly deploy the target environment file in the second container, thereby increasing the speed of creating the operating environment of the second container and reducing the latency when the user is using it.

[0093] Furthermore, when the first container and the second container do not meet the affinity rule, affinity scheduling cannot be used directly. In order to improve the speed of creating the operating environment of the second container, the present invention uses a P2P network to quickly obtain the target environment file, and then quickly deploys the target environment file in the second container, so as to improve the speed of creating the operating environment of the second container and reduce the delay when the user uses it.

[0094] In one embodiment, step S4 includes: Step S41: Create a third container based on the GPU resources of the intelligent computing center; Step S42: Processing the adjusted model code based on the third container to obtain a processing result; After step S4, the method further includes: Step S5: Send the processing result to the terminal, close the third container, and release the GPU resources of the intelligent computing center occupied by the third container.

[0095] In the present invention, a third container is created based on the GPU resources of the intelligent computing center; the adjusted model code is processed based on the third container to obtain a processing result; the processing result is sent to the terminal, and the third container is closed to release the GPU resources of the intelligent computing center occupied by the third container. In this way, the adjusted model code is run by occupying the GPU computing resources of the intelligent computing center through the third container, and the computing resources of the intelligent computing center are released after the operation is completed, thereby improving the utilization rate of the computing resources of the intelligent computing center.

[0096] Furthermore, the computing power resources of the intelligent computing center are released after the processing results are obtained, reducing the time users need to rent computing power resources and realizing the widespread application of inclusive computing power.

[0097] In some implementations, the processing result includes an operation log, so that the user can determine the operation status of the updated model code through the operation log, so as to further optimize the model code.

[0098] It should be noted that the method of the present invention achieves consistency guarantee of the operating environment, code, and data, and users can quickly use inclusive computing power with one click. Specifically, before step S41, the method includes: Step S40: receiving a parameter instruction sent by the terminal, wherein the parameter instruction includes a required parameter for running the model code; Step S42 includes: Step S421: Process the updated model code based on the container and the requirement parameters to obtain a processing result.

[0099] The requirement parameters may include operating parameters, and the intelligent computing center can infer the operating environment and resource size required for the development task based on the operating parameters and the updated model code.

[0100] Furthermore, the model code can be updated and the updated model code can be processed based on the image-based ultra-fast loading technology, which can achieve millisecond-level startup, making users unaware of the existence of the remote intelligent computing center and improving the user experience.

[0101] In some implementations, the intelligent computing center can have multiple containers share the computing power resources of a GPU accelerator card, which can further reduce user usage costs and the system load of the intelligent computing center.

[0102] See also Figure 2 , Figure 2This is a structural diagram of a remote development device for an intelligent computing center model for universal computing power provided by the present invention, such as Figure 2 As shown, the intelligent computing center model remote development device 200 for inclusive computing power includes: A generation module 201 is used to adjust the operating environment of the first container based on the operating information when the operating information is received, and generate a target environment file, wherein the target environment file is used to characterize the relevant conditions of the operating environment, wherein the first container is a container created based on the central processing unit CPU resources of the intelligent computing center; A creation module 202, configured to create an operating environment of a second container based on the target environment file when a code adjustment instruction is received, wherein the second container is a container created based on the CPU resources of the intelligent computing center; An adjustment module 203, configured to adjust the model code in the running environment of the second container based on the code adjustment instruction; The processing module 204 is used to schedule the graphics processor GPU resources of the intelligent computing center to process the adjusted model code.

[0103] In one embodiment, the generating module 201 includes: An acquisition unit, used to acquire an initial environment file, where the initial environment file is a file corresponding to the container when the model code is last adjusted; A first adjustment unit, configured to adjust the operating environment of the first container based on the operation information when receiving the operation information, to obtain an adjustment result; The first saving unit is used to save the adjustment result in layers to the initial environment file to obtain the target environment file when the adjustment result meets the first preset condition.

[0104] In one embodiment, the first preset condition includes at least one of the following: The data volume of the adjustment result is greater than or equal to a first set data threshold; The time since the last adjustment result was saved is greater than or equal to the set time threshold.

[0105] In one embodiment, the initial environment file includes at least one storage layer, and the first storage unit includes: A traversal subunit, used for traversing at least one storage layer of the initial environment file; a determination subunit, configured to determine a target preservation layer, wherein the target preservation layer is a preservation layer that satisfies a second preset condition among the at least one preservation layer; The saving subunit is used to save the adjustment result to the target saving layer to obtain the target environment file.

[0106] In one embodiment, the second preset condition includes at least one of the following: The data volume of the target storage layer is less than or equal to a second set data threshold; The sum of the data volume of the target storage layer and the data volume of the adjustment result is less than or equal to a third set data threshold; The number of layers of the target preservation layer is less than or equal to a set number threshold.

[0107] In one embodiment, the generating module 201 includes: A second adjustment unit is configured to adjust the operating environment of the first container based on the operation information to obtain an adjustment result when the operation information and the save instruction are received; The second saving unit is used to save the multiple processes of the first container after the operating environment is adjusted, and the memory information corresponding to each process to obtain the target environment file, at least one process among the multiple processes is a process corresponding to the adjustment result.

[0108] In one embodiment, the creation module 202 includes at least one of the following: a first creating unit, configured to, when the first container and the second container satisfy an affinity rule, perform affinity scheduling on the target environment file, and create a running environment of the second container based on the target environment file obtained by the scheduling; The second creating unit is configured to schedule the target environment file based on a peer-to-peer P2P network when the first container and the second container do not satisfy the affinity rule, and create a running environment of the second container based on the target environment file obtained through scheduling.

[0109] In one embodiment, the processing module 204 includes: A third creation unit, configured to create a third container based on the GPU resources of the intelligent computing center; A processing unit, configured to process the adjusted model code based on the third container to obtain a processing result; After the processing module 204, the intelligent computing center model remote development device 200 for inclusive computing power further includes: A release module is used to send the processing result to the terminal, close the third container, and release the GPU resources of the intelligent computing center occupied by the third container.

[0110] The remote development device of the intelligent computing center model for inclusive computing power provided by the present invention can realize the various processes of the various embodiments of the above-mentioned remote development method of the intelligent computing center model for inclusive computing power. The technical features correspond one to one and can achieve the same technical effects. To avoid repetition, they will not be described here.

[0111] It should be noted that the remote development device of the intelligent computing center model for inclusive computing power in the present invention can be a device, or a component, integrated circuit, or chip in an electronic device.

[0112] The present invention also provides an electronic device, see Figure 3 , Figure 3 is a schematic diagram of the structure of an electronic device provided by the present invention, the electronic device includes a memory 301, a processor 302 and a program or instruction stored in the memory 301 and running thereon, and when the program or instruction is executed by the processor 302, it can be realized Figure 1 Any steps in the corresponding embodiment of the remote development method of the intelligent computing center model for inclusive computing power and the same beneficial effects are achieved will not be repeated here.

[0113] The processor 302 may be a CPU, an ASIC, an FPGA or a GPU.

[0114] A person of ordinary skill in the art can understand that all or part of the steps of the above-mentioned embodiment of the remote development method of the intelligent computing center model for inclusive computing power can be completed by hardware related to program instructions, and the program can be stored in a readable medium.

[0115] The present invention also provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above Figure 1 Any steps in the corresponding embodiments of the remote development method of the intelligent computing center model for inclusive computing power can achieve the same technical effect. To avoid repetition, they will not be repeated here. The storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0116] The present invention also provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the above Figure 1 The corresponding processes of the implementation method of the remote development method of the intelligent computing center model for inclusive computing power can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0117] The terms "first", "second" etc. in the present invention are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. In addition, the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. In addition, "and / or" is used in the present application to represent at least one of the connected objects, such as A and / or B and / or C, which means to include 7 situations including single A, single B, single C, and A and B all exist, B and C all exist, A and C all exist, and A, B and C all exist.

[0118] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0119] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a second terminal device, etc.) to execute the methods of each embodiment of the present application.

[0120] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A remote development method of an intelligent computing center model for universal computing power, characterized in that: include: Step S1: When operation information is received, the operating environment of the first container is adjusted based on the operation information, and a target environment file is generated, where the target environment file is used to characterize relevant conditions of the operating environment, and the first container is a container created based on the central processing unit CPU resources of the intelligent computing center; Step S2: when receiving the code adjustment instruction, creating an operating environment of a second container based on the target environment file, where the second container is a container created based on the CPU resources of the intelligent computing center; Step S3: adjusting the model code in the running environment of the second container based on the code adjustment instruction; Step S4: dispatching the graphics processor GPU resources of the intelligent computing center to process the adjusted model code.

2. The method according to claim 1, characterized in that The step S1 comprises: Step S11, obtaining an initial environment file, wherein the initial environment file is a file corresponding to the container when the model code is last adjusted; Step S12: when receiving the operation information, adjusting the operating environment of the first container based on the operation information to obtain an adjustment result; Step S13: When the adjustment result meets the first preset condition, the adjustment result is saved in layers to the initial environment file to obtain the target environment file.

3. The method according to claim 2, characterized in that The first preset condition includes at least one of the following: The data volume of the adjustment result is greater than or equal to a first set data threshold; The time since the last adjustment result was saved is greater than or equal to the set time threshold.

4. The method according to claim 2 or 3, characterized in that The initial environment file includes at least one storage layer, and the step S13 includes: Step S131, traversing at least one storage layer of the initial environment file; Step S132: determining a target preservation layer, wherein the target preservation layer is a preservation layer that satisfies a second preset condition among the at least one preservation layer; Step S133: Save the adjustment result to the target storage layer to obtain the target environment file.

5. The method according to claim 4, characterized in that The second preset condition includes at least one of the following: The data volume of the target storage layer is less than or equal to a second set data threshold; The sum of the data volume of the target storage layer and the data volume of the adjustment result is less than or equal to a third set data threshold; The number of layers of the target preservation layer is less than or equal to a set number threshold.

6. The method according to claim 1, characterized in that The step S1 comprises: Step S14: when the operation information is received and a save instruction is received, the operating environment of the first container is adjusted based on the operation information to obtain an adjustment result; Step S15: Save the multiple processes of the first container after adjusting the running environment and the memory information corresponding to each process to obtain the target environment file, wherein at least one process among the multiple processes is a process corresponding to the adjustment result.

7. The method according to any one of claims 1, 2, 3 or 6, characterized in that: The step S2 includes at least one of the following: Step S21: When the first container and the second container satisfy the affinity rule, affinity scheduling is performed on the target environment file, and a running environment of the second container is created based on the target environment file obtained by scheduling; Step S22: If the first container and the second container do not satisfy the affinity rule, schedule the target environment file based on a peer-to-peer P2P network, and create a running environment for the second container based on the scheduled target environment file.

8. The method according to any one of claims 1, 2, 3 or 6, characterized in that: The step S4 comprises: Step S41: Create a third container based on the GPU resources of the intelligent computing center; Step S42: Processing the adjusted model code based on the third container to obtain a processing result; After step S4, the method further includes: Step S5: Send the processing result to the terminal, close the third container, and release the GPU resources of the intelligent computing center occupied by the third container.

9. A remote development device for an intelligent computing center model for universal computing power, characterized in that: include: A generation module, configured to adjust the operating environment of the first container based on the operating information when the operating information is received, and generate a target environment file, wherein the target environment file is used to characterize relevant conditions of the operating environment, wherein the first container is a container created based on the central processing unit (CPU) resources of the intelligent computing center; A creation module, configured to create an operating environment of a second container based on the target environment file when a code adjustment instruction is received, wherein the second container is a container created based on the CPU resources of the intelligent computing center; An adjustment module, configured to adjust the model code in the running environment of the second container based on the code adjustment instruction; The processing module is used to schedule the graphics processor GPU resources of the intelligent computing center to process the adjusted model code.

10. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the remote development method of an intelligent computing center model for inclusive computing power as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the remote development method of an intelligent computing center model for inclusive computing power as described in any one of claims 1 to 8.

12. A computer program product, characterized in that It includes computer instructions, which, when executed by a processor, implement the steps of the remote development method of an intelligent computing center model for universal computing power as described in any one of claims 1 to 8.

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